# Maisa AI > --- ## Pages - [AI Computer](https://maisa.ai/ai-computer/): An AI Computer is a new computing paradigm, where AI acts as the core orchestrator. It manages tools, data, and tasks to deliver real outcomes, not just answers - [Agentic Process Automation](https://maisa.ai/agentic-process-automation/): Agentic Process Automation extends automation beyond repetitive tasks, enabling AI agents to handle exceptions, and complex decisions. - [Chain of Work](https://maisa.ai/chain-of-work/): Chain of Work logs every AI decision and action, creating deterministic workflows that prevent hallucinations and ensure transparent, reliable outputs. - [AI Agents](https://maisa.ai/ai-agents/): AI agents are systems that plan, act with tools, and learn, covering key components, capabilities, challenges, and the role of digital workers in business. - [AI Hallucinations](https://maisa.ai/ai-hallucinations/): AI hallucinations produce plausible but false content; causes and fixes include larger data, CoT logic, RAG grounding, and Maisa’s deterministic approach. - [Digital Workers](https://maisa.ai/digital-workers/): Digital Workers are AI agents for business processes that adapt, collaborate, and log every step, providing accountable, transparent automation. - [Introducing Vinci KPU](https://maisa.ai/research/): Comprehensive overview of Maisa AI's Vinci KPU. Read detailed benchmark comparisons, architecture improvements, and new features. Includes performance analysis on GPQA, MATH, HumanEval, and ProcBenc. - [Legal Notice](https://maisa.ai/legal-notice/): "This legal notice (the "Legal Notice") governs access to and navigation of the website www. maisa. ai (the "Website"). The... - [Cookie Policy](https://maisa.ai/cookie-policy/): MAISA INC. , (hereinafter "MAISA") is the owner of the Website https://maisa. ai/ (hereinafter the "Website") and it’s the owner... - [Terms of Service](https://maisa.ai/terms-of-service/): These Terms of Service ("Agreement") are the agreement governing your access to and use of the Services as defined below.... - [Contact](https://maisa.ai/contact/): - [Agentic Insights](https://maisa.ai/agentic-insights/): - [Careers](https://maisa.ai/careers/): - [Manifesto](https://maisa.ai/about-us/): - [Maisa AI - Agentic Process Automation - Agents - Digital Workers](https://maisa.ai/): Self-healing Agentic Process Automation with full control & traceability. Delegate to Digital Workers that continuously learn and improve, ensure full auditability, and retain the know-how of your processes. --- ## Posts - [From tasks to outcomes: Use cases for Agentic Process Automation](https://maisa.ai/agentic-insights/agentic-process-automation-use-cases/): Agentic Process Automation moves beyond task bots, automating entire workflows that involve judgment, multi-step logic, and cross-tool coordination. - [Understanding the benefits of Agentic Process Automation](https://maisa.ai/agentic-insights/benefits-agentic-process-automation/): Agentic Process Automation delivers adaptive, goal-driven workflows: cross-system orchestration, self-learning, resilience, and faster time to value - [RPA vs Agentic Process Automation: What’s the difference?](https://maisa.ai/agentic-insights/rpa-vs-agentic-process-automation/): RPA vs Agentic Process Automation shows how rule-driven bots differ from autonomous AI agents that adapt, reason, and scale across complex workflows. - [The promise and complexity of Multi-Agent AI](https://maisa.ai/agentic-insights/multi-agent-ai/): Multi-Agent AI boosts performance through specialization and parallelism, yet faces context limits, fragmentation, and management challenges. - [HALP: Maisa’s breakthrough in delivering reliability for enterprise automation](https://maisa.ai/agentic-insights/halp/): HALP lets AI learn from real work, not datasets. Digital Workers absorb business logic through live feedback, delivering reliable, traceable automation. - [How fast is fast? A simpler way to deploy AI automation](https://maisa.ai/agentic-insights/fast-ai-automation-deployment/): A simpler way to deploy AI automation: define goals in plain language, let Digital Workers build tasks, reducing integration from months to weeks. - [Why we built Maisa this way: scientific proof we're on the right track](https://maisa.ai/agentic-insights/science-behind-maisa-architecture/): The architecture behind Maisa is the result of deliberate choices informed by research. A growing body of work has made... - [Digital Workers: bringing accountability to AI agency](https://maisa.ai/agentic-insights/digital-workers/): We keep hearing the term "AI Agents" everywhere these days. LinkedIn is flooded with posts about them. Tech conferences can't... - [What happens when AI forgets? Context windows and their limits](https://maisa.ai/agentic-insights/ai-context-limitations/): AI can write emails, summarize research, or help you brainstorm ideas. It feels smart and useful. But for any of... - [Advancing our vision for Accountable AI together with Microsoft](https://maisa.ai/agentic-insights/microsoft-partnership/): Maisa joins Microsoft for Startups Founders Hub and becomes a Strategic Partner, advancing Accountable AI and expanding access to its Digital Workers via the Azure Marketplace. - [Black Box AI. How can we trust what we can’t see?](https://maisa.ai/agentic-insights/black-box-ai/): Lack of transparency in black box AI models complicates business decision-making, regulatory adherence, and trust in outcomes derived from internal data. - [The AI Computer: overcoming fundamental AI challenges](https://maisa.ai/agentic-insights/ai-computer-overcoming-ai-challenges/): The AI Computer marks a shift in computing, where AI moves beyond chatbots to orchestrate tasks, tools, and processes. - [What is Agentic Process Automation? The next frontier of Intelligent Automation](https://maisa.ai/agentic-insights/what-is-agentic-process-automation/): At the core of every organization lies a foundation of business processes. While essential, these processes often involve mundane, repetitive... - [Making AI accountable: Maisa raises pre-seed round](https://maisa.ai/agentic-insights/maisa-raises-pre-seed-round/): Back in March, we introduced the first version of the KPU, setting new benchmarks that surpassed leading models. Since then,... - [Introducing Vinci KPU](https://maisa.ai/agentic-insights/vinci-kpu/): Comprehensive overview of Maisa AI's Vinci KPU. Read detailed benchmark comparisons, architecture improvements, and new features. Includes performance analysis on GPQA, MATH, HumanEval, and ProcBenc. - [Hello world](https://maisa.ai/agentic-insights/hello-world/): Hello World In recent periods, the community has observed an almost exponential enhancement in the proficiency of Artificial Intelligence, notably... --- # # Detailed Content ## Pages ### AI Computer > An AI Computer is a new computing paradigm, where AI acts as the core orchestrator. It manages tools, data, and tasks to deliver real outcomes, not just answers - Published: 2025-05-09 - Modified: 2025-06-06 - URL: https://maisa.ai/ai-computer/ - Translation Priorities: Optional --- ### Agentic Process Automation > Agentic Process Automation extends automation beyond repetitive tasks, enabling AI agents to handle exceptions, and complex decisions. - Published: 2025-05-09 - Modified: 2025-06-06 - URL: https://maisa.ai/agentic-process-automation/ - Translation Priorities: Optional --- ### Chain of Work > Chain of Work logs every AI decision and action, creating deterministic workflows that prevent hallucinations and ensure transparent, reliable outputs. - Published: 2025-04-21 - Modified: 2025-06-06 - URL: https://maisa.ai/chain-of-work/ - Translation Priorities: Optional --- ### AI Agents > AI agents are systems that plan, act with tools, and learn, covering key components, capabilities, challenges, and the role of digital workers in business. - Published: 2025-04-21 - Modified: 2025-06-06 - URL: https://maisa.ai/ai-agents/ - Translation Priorities: Optional --- ### AI Hallucinations > AI hallucinations produce plausible but false content; causes and fixes include larger data, CoT logic, RAG grounding, and Maisa’s deterministic approach. - Published: 2025-04-16 - Modified: 2025-06-06 - URL: https://maisa.ai/ai-hallucinations/ - Translation Priorities: Optional --- ### Digital Workers > Digital Workers are AI agents for business processes that adapt, collaborate, and log every step, providing accountable, transparent automation. - Published: 2025-04-11 - Modified: 2025-06-06 - URL: https://maisa.ai/digital-workers/ - Translation Priorities: Optional --- ### Introducing Vinci KPU > Comprehensive overview of Maisa AI's Vinci KPU. Read detailed benchmark comparisons, architecture improvements, and new features. Includes performance analysis on GPQA, MATH, HumanEval, and ProcBenc. - Published: 2024-11-26 - Modified: 2025-04-15 - URL: https://maisa.ai/research/ - Translation Priorities: Optional --- ### Legal Notice - Published: 2024-10-28 - Modified: 2025-03-26 - URL: https://maisa.ai/legal-notice/ - Translation Priorities: Optional "This legal notice (the "Legal Notice") governs access to and navigation of the website www. maisa. ai (the "Website"). The Website is owned by Maisa Inc. , ("Maisa" or "we"), whose identifying and contact information is as follows: Address: 8 The Green STE R, Dover, Kent County, Delaware 19901. Contact email address: contact@Maisa. ai REG & C. o. Incorporation: State of Delaware, Division of Incorporations SR20233303552FN7632442 This Legal Notice is binding for anyone accessing the Website (the "user" or "you"). Please note that by browsing the Website, you acknowledge that you have read and agree to be bound by the following documents: this Legal Notice, our Privacy Policy, and our Cookie Policy. If you do not agree with any of these texts, you should not access or use the Website. The original version of this Legal Notice has been drafted in Spanish. However, Maisa Inc. may, as a courtesy, provide users with versions of this Legal Notice in other languages (for example, in English). In case of contradiction between versions, the Spanish version will prevail. CONDITIONS OF ACCESS AND USE OF THE WEBSITE Access to and use of the Website is only permitted for individuals eighteen (18) years of age or older. Access to and use of the Website do not require the creation of a user account. However, in the future, Maisa Inc. may incorporate restricted sections or functionalities that do require user registration. INTELLECTUAL AND INDUSTRIAL PROPERTY Maisa Inc. holds the intellectual and industrial property rights over the Website and all its related elements. This includes, for example: All rights to the source code, object code, interface, databases, and other elements of the Website. All content on the Website (images, texts, videos, etc. ). All rights to the trademarks, trade names, and other distinctive signs of Maisa Inc. Users are not authorized to reproduce, distribute, publicly communicate, or transform the Website or its contents. By way of example, this means that users may not extract or reuse, in whole or in part, the information available on the Website, regardless of whether the extraction is done through automated techniques (screen-scraping, bots, spiders, etc. ) or manually. PERMITTED USES OF THE WEBSITE As a user of the Website, you declare and warrant that you will make appropriate use of it. The following list includes, for example, some of the commitments you undertake: You will not use the Website to transmit or install viruses or other harmful elements. You will not attempt to access restricted sections of the Website or its systems and networks. You will not try to breach the security or authentication measures of the Website. You will not replicate or reverse engineer or decompile the Website (except in cases where the law expressly authorizes it). You will not engage in abusive use of the Website or use it in a way that could cause saturation of the Website. You will not use the Website to extract information that allows you to offer a product or service (analog or... --- ### Cookie Policy - Published: 2024-10-28 - Modified: 2025-03-26 - URL: https://maisa.ai/cookie-policy/ - Translation Priorities: Optional MAISA INC. , (hereinafter "MAISA") is the owner of the Website https://maisa. ai/ (hereinafter the "Website") and it’s the owner of the platform https://platform. maisa. ai/ (hereinafter the “Platform”). Both of them use cookies that collect information related to the connection, browsers, and devices used by Internet users who access or use the Website and/or the Platform (hereinafter the "User/s"). MAISA uses this information to manage and improve the proper functioning of the Website and/or the Platform. This Policy describes what information these cookies collect, how they are used and for what purpose. It also indicates how the User can restrict or block the automatic downloading of cookies, however, this could reduce or even hinder certain elements of the functionality of the Website and/or the Platform. Likewise, the User can choose the category of cookies that he/she wishes to activate in the cookies banner that appears the first time he/she accesses the Website and/or the Platform. 1. DEFINITION OF COOKIES Cookies are small text files that are placed on the User's computer, smartphone or other device when accessing the Internet. This is done to improve the User's experience and for other purposes, such as recognizing Users when accessing the Website and/or the Platform, ensuring the security of your account and delivering targeted advertising. For more general information about cookies, please see the following article. 2. HOW WE USE COOKIES In summary, MAISA uses the cookies listed in Annex I for the Website and the cookies listed in Annex II of this Policy for the Platform to track how the Website and/or the Platform is used in order to optimize its operation. 3. WHAT COOKIES WE USE The Website and/or the Platform use both its own and third-party cookies: First-party cookies: cookies sent to your device by MAISA through the web domain. Third-party cookies: these are sent to your device by domains that are not managed by MAISA but by another entity that processes the data collected through cookies. According to the purpose of the cookies, the cookies used by MAISA can be divided into the following categories: Technical cookies (necessary): cookies necessary for navigation and for the proper functioning of the Website and/or the Platform. Their use allows basic functions, such as access and secure navigation. The legal basis that allows the collection of data through these cookies is the legitimate interest of MAISA in the management of the Website and/or the Platform. No information collected through these cookies is shared with third parties. See the cookie table below for more details of these cookies. Analytical cookies: allow monitoring and analyzing the behavior of Users. The information collected through this type of cookies is used to measure the activity of the Website and/or the Platform and for the elaboration of browsing profiles of the Users, in order to improve the Website and/or the Platform and their services. The legal basis for collecting this data through these cookies is the consent of the User. See the table of cookies below for... --- ### Terms of Service - Published: 2024-10-28 - Modified: 2025-03-27 - URL: https://maisa.ai/terms-of-service/ - Translation Priorities: Optional These Terms of Service ("Agreement") are the agreement governing your access to and use of the Services as defined below. This Agreement is between Maisa, Inc, a Delaware corporation, with offices at 1111B S Governors Ave STE 3624 Dover, DE 19904 ("Maisa"), and the entity you represent by entering into this Agreement ("Customer"). Any capitalized terms not defined throughout the Agreement will have the meaning given to them in Section 17 (Definitions). This Agreement is effective upon the earlier of (i) your acceptance of this Agreement, or (ii) the date you first accessed the Services, as applicable ("Effective Date"), and will remain in effect until terminated in accordance with this Agreement. Binding Effect By using the Services hosted in the Platform and/or entering into this Agreement, you represent and warrant that (i) you have read and understand this Agreement, (ii) you understand that the Services provided under this Agreement are for businesses, professionals and developers, not consumers, (iii) you are not a consumer as defined under applicable laws, (iv) you have full legal authority to bind Customer to this Agreement, and (v) you agree to this Agreement on behalf of Customer. If you or Customer do not agree with this Agreement, please refrain from accepting this Agreement and from using the Services. Services Provision of Services. During the Term, Customer will have access to Maisa's web-based artificial intelligence-powered studio ("Studio") for the purpose of creating, configuring, and deploying multi-modal AI agentic cloud functions or Digital Workers ("Agents") on the Platform (collectively, the "Services") in accordance with this Agreement. Use of Services. Customer agrees only to use the Services in accordance with this Agreement. Customer's use of the Services may include deploying the Services to develop Customer Applications and making available Customer Applications to End Users, provided, however, that Customer may not sublicense the Agents or the Services as a standalone or integrated product. Customer will ensure that End User's use of the Services complies with this Agreement. Sign up/Account. Customer or End User must sign up on the Platform to create an account ("Account") to use the Services. The Customer may do so by synchronizing its Google or Microsoft account or by completing the data fields requested by Maisa (name, surname, email) which will be processed in accordance with the Privacy Policy. Customer is solely responsible for all activities that occur under its Account, including using, managing and protecting the Account, including its security, both by Customer and End Users. Customer will not (i) disclose or otherwise share Account access credentials with unauthorized third parties, (ii) share individual login credentials between multiple users on an Account, or (iii) resell or lease access to its account. Customer will (a) promptly notify Maisa if it becomes aware of any unauthorized access to or use of Customer's account or the Services and (b) use commercially reasonable efforts to prevent and terminate such unauthorized access to our use. Consent. Customer is solely responsible for obtaining any consent or providing notices required (i) for Customer... --- ### Contact - Published: 2024-10-25 - Modified: 2025-03-20 - URL: https://maisa.ai/contact/ - Translation Priorities: Optional --- ### Agentic Insights - Published: 2024-10-25 - Modified: 2025-03-06 - URL: https://maisa.ai/agentic-insights/ - Translation Priorities: Optional --- ### Careers - Published: 2024-10-25 - Modified: 2025-06-09 - URL: https://maisa.ai/careers/ - Translation Priorities: Optional --- ### Manifesto - Published: 2024-10-25 - Modified: 2025-02-27 - URL: https://maisa.ai/about-us/ - Translation Priorities: Optional --- ### Maisa AI - Agentic Process Automation - Agents - Digital Workers > Self-healing Agentic Process Automation with full control & traceability. Delegate to Digital Workers that continuously learn and improve, ensure full auditability, and retain the know-how of your processes. - Published: 2024-10-25 - Modified: 2025-06-02 - URL: https://maisa.ai/ - Translation Priorities: Optional --- --- ## Posts ### From tasks to outcomes: Use cases for Agentic Process Automation > Agentic Process Automation moves beyond task bots, automating entire workflows that involve judgment, multi-step logic, and cross-tool coordination. - Published: 2025-06-12 - Modified: 2025-06-13 - URL: https://maisa.ai/agentic-insights/agentic-process-automation-use-cases/ - Translation Priorities: Optional Automation has traditionally focused on simple, repetitive tasks that follow predictable rules. While this approach has streamlined many routine jobs, critical business processes still rely heavily on manual intervention. These workflows typically involve judgment, changing inputs, or coordination across multiple tools and teams, making traditional automation ineffective. Agentic Process Automation (APA) closes this gap, using AI agents to handle complex processes needing contextual reasoning and multi-step decisions. Instead of rigid scripts, APA adapts to changing information, learns from human feedback, and coordinates actions across different tools, making automation smarter and more flexible than ever. What’s possible to automate with Agentic Process Automation Agentic Process Automation is built for the kinds of workflows that most automation tools avoid, the ones that aren’t fully predictable. These are processes where rules can’t be hardcoded, because the right action depends on changing data, uncertain conditions, or outcomes that unfold step by step. APA handles entire workflows that involve: Judgment or ambiguity, like deciding if a customer message should be escalated or answered directly Coordination across tools, like pulling data from an ERP, updating a spreadsheet, and sending a follow-up email Multi-step logic, where each step depends on the outcome of the last Instead of automating isolated tasks, APA connects the dots across systems and decisions. It can understand context, make decisions in real time, and adjust its actions based on what it sees. That’s what makes it effective for the kinds of processes that still live in spreadsheets, inboxes, or manual reviews. Use cases that show APA in action Across many teams, there’s a growing need to automate work that doesn’t follow a fixed script, where the right next step depends on the data, the tools involved, or the situation itself. These examples show how AI agents are already taking on that middle ground, handling real operations in finance, customer service, and supply chains, where rules aren’t always fixed and context matters. Invoice Processing & Accounts Payable What’s the business problem? Finance teams spend hours reviewing invoices by hand. Formats vary, data needs to be verified, and mismatches often slow down payments or lead to errors. How does APA solve it? APA reads any invoice format, extracts the relevant data, and matches it with POs and receipts from the ERP. If everything lines up, it posts the transaction. If not, it flags the discrepancy, explains it, and routes it for human approval. It also learns from corrections to improve over time. What systems/tools are involved? Email, ERP (SAP, Oracle), shared inboxes, and approval workflows. Why traditional automation falls short Traditional systems need fixed templates or strict formatting rules. APA handles unstructured documents, adapts to new formats, and reasons across systems without rigid scripts. What improvements can be expected? Faster processing, fewer errors, and more invoices going straight through without human review. Customer Support Triage What’s the business problem? Support teams face a growing number of incoming requests. Tickets get routed inconsistently, and agents spend too much time on repetitive questions. How does APA solve... --- ### Understanding the benefits of Agentic Process Automation > Agentic Process Automation delivers adaptive, goal-driven workflows: cross-system orchestration, self-learning, resilience, and faster time to value - Published: 2025-06-11 - Modified: 2025-06-12 - URL: https://maisa.ai/agentic-insights/benefits-agentic-process-automation/ - Translation Priorities: Optional Traditional automation has transformed many repetitive tasks into seamless routines. However, businesses today face more complex challenges: rapid market shifts, evolving customer expectations, and the constant demand for flexibility. Simple rule-based systems often fall short when processes become dynamic and unpredictable. Agentic Process Automation (APA) addresses this gap by introducing goal-oriented AI agents that independently plan, adjust, and act across diverse systems. Instead of following fixed instructions, these agents adapt to real-time changes and improve continuously based on experience. This new approach unlocks significant opportunities for companies, making operations faster, more resilient, and better suited to leveraging human talent strategically. What businesses gain from Agentic Process Automation End-to-End orchestration across silos Businesses typically use several systems like ERP, CRM, and legacy software, often requiring manual coordination and creating duplicate data. Agentic Process Automation (APA) simplifies this complexity by linking all systems through one intelligent workflow. APA agents autonomously manage tasks across these platforms, eliminating repetitive manual efforts and data silos. For your business, this means clearer visibility, fewer mistakes, and quicker, smoother execution across processes. Resilience and adaptability Processes frequently encounter issues such as data errors or workflow interruptions. Agentic Process Automation (APA) addresses these by automatically detecting and correcting errors as they occur. APA agents also adjust workflows dynamically when situations change, such as during supply chain delays or shifts in customer demand. This reduces downtime, ensures processes remain reliable, and keeps your operations running smoothly without needing constant manual supervision. Continuous self-optimisation Unlike static automation that requires regular updates, APA systems learn from every task they perform. Each execution provides feedback, helping APA agents automatically improve their performance over time. This means your automation continuously becomes faster and more accurate, reducing the need for ongoing manual maintenance or reengineering. Giving teams back their focus Agentic Process Automation reduces the burden of repetitive, tedious tasks that often lead to mistakes or burnout. By handling routine, error-prone activities, APA improves reliability and allows employees to spend less time on busywork. This creates a more effective work environment where teams can focus on quality and performance, rather than simply managing workloads. Faster deployment and time to value Agentic Process Automation allows you to define objectives in natural language, making setup intuitive and quick. APA agents understand context and autonomously determine how to achieve your goals, significantly reducing the complexity of initial configuration. This streamlined approach means your business can rapidly implement new processes, easily iterate, and quickly see measurable results. A note on the challenges Agentic Process Automation offers significant benefits, but it still comes with important challenges. APA systems rely on probabilistic models, which means decisions can sometimes be difficult to predict or explain clearly. Without proper safeguards, these systems can produce unexpected results or inconsistencies, impacting reliability and trust. Ensuring transparency, explainability, and clear accountability is essential, especially for critical or regulated business processes. For businesses adopting APA, carefully balancing automation with human oversight remains crucial to maintaining control, reliability, and trust. The strategic value of APA Agentic Process... --- ### RPA vs Agentic Process Automation: What’s the difference? > RPA vs Agentic Process Automation shows how rule-driven bots differ from autonomous AI agents that adapt, reason, and scale across complex workflows. - Published: 2025-06-10 - Modified: 2025-06-12 - URL: https://maisa.ai/agentic-insights/rpa-vs-agentic-process-automation/ - Translation Priorities: Optional Humans have always looked for ways to ease the burden of repetitive, time-consuming work. This impulse sparked industrial revolutions, mechanizing physical tasks and freeing countless people from manual labor. As machines took over physical workloads, our roles evolved toward knowledge-based tasks. However, a new kind of repetitive work emerged: digital tasks such as copying data, pasting information, updating multiple systems, and moving details around. In this digital landscape, we naturally sought ways to automate office tasks just as we had automated physical labor. Initially, only specialized engineers or IT teams could build the tools necessary to reduce this digital burden. In the 2010s, Robotic Process Automation (RPA) emerged as a practical solution, making automation more accessible. RPA software mimics human actions at scale, performing routine tasks efficiently and reliably, especially in large enterprises. Yet, despite RPA’s benefits, many digital tasks still persistently consume our time and attention. What if automation could do more than just follow explicit instructions? Could automation adapt to changing situations, reason through complex problems, and collaborate dynamically, much like humans do? Exploring this possibility leads us to the next chapter: Agentic Process Automation. What is RPA Robotic Process Automation (RPA) is software designed to mimic the actions humans take on a computer. It follows predefined steps exactly as instructed, performing tasks like entering data, processing invoices, or scraping information from screens. RPA works well for repetitive, predictable tasks because it consistently executes rules without variation or fatigue. If a process is structured, clear, and rule-based, RPA can reliably handle it, saving significant time and reducing errors that come from manual input. Limitations of RPA Robotic Process Automation is effective for structured, repetitive tasks, but it quickly hits its limits when faced with the complexities of real-world business operations. Many business processes involve ambiguity, unpredictable changes, and unstructured data such as emails, documents, or customer messages. RPA cannot understand or adapt to these nuances, it strictly follows predefined steps. As businesses grow and evolve, these rigidly scripted automations can become obstacles rather than solutions. Minor changes in the process or system can cause RPA to break, requiring constant maintenance and manual intervention. Ultimately, while RPA excels at simple tasks, its inability to reason, adapt, or interpret context makes it inadequate for complex, dynamic workflows. Agentic Process Automation Agentic Process Automation (APA) introduces a fundamentally new approach to automation. Unlike traditional methods, APA uses autonomous AI agents that adapt, decide, and act based on goals rather than predefined scripts. This means shifting the focus from specifying each step to clearly defining what you want to achieve. APA agents are goal-driven; you tell them the outcomes you need, and they determine the best way to achieve those outcomes. They are context-aware, continuously adjusting their actions to handle changes or unexpected events smoothly. APA agents are also intelligent, they can interpret data, reason through ambiguity, and make informed decisions in real-time. Moreover, APA continuously improves by learning from each task it completes, becoming more efficient and effective over time. Robotic... --- ### The promise and complexity of Multi-Agent AI > Multi-Agent AI boosts performance through specialization and parallelism, yet faces context limits, fragmentation, and management challenges. - Published: 2025-06-05 - Modified: 2025-06-05 - URL: https://maisa.ai/agentic-insights/multi-agent-ai/ - Translation Priorities: Optional AI agents are transforming knowledge work and automation, reshaping how we handle tasks from customer interactions to complex business processes. Multi-agent frameworks, in which several specialized AI agents collaborate, are increasingly capturing attention as a promising approach to scaling and enhancing performance. While multi-agent frameworks can offer valuable solutions in certain scenarios, they also bring specific challenges, including inherent AI issues like hallucinations and context limitations, as well as increased system complexity. When designing AI systems, understanding these tradeoffs clearly is essential to ensure effective outcomes. What is a multi-agent framework? A multi-agent framework is an AI system consisting of multiple AI agents working collaboratively to achieve a common objective. Each agent is specialized in a distinct task, allowing complex processes to be divided into simpler, more focused actions. For example, in a document review workflow, a Data Extraction Agent converts raw documents into structured text. A Summarizer Agent highlights key points and condenses information. A Validator Agent ensures accuracy, consistency, and adherence to guidelines. Finally, a Monitor Agent tracks progress and flags issues. Context, clarity, and collaboration in multi-agent systems Multi-agent frameworks excel primarily due to specialization. AI systems deliver superior results when tasked with clearly defined, specific goals. This specificity principle underlies effective prompt design: concise and targeted instructions yield far more precise outcomes. For instance, asking ChatGPT to create an outline for an article produces consistently better results than instructing it to craft an entire marketing campaign. The reason behind this effectiveness lies in context. AI models process information within a limited context window, which determines how much data they can handle at a given time. Within this window, the AI prioritizes relevant information and disregards unnecessary details. When tasks are too broad or complex, important details may become diluted or overlooked, reducing the quality of the output. By dividing a task into specialized subtasks, each agent receives targeted information, reducing clutter and improving the model's focus and accuracy. Some multi-agent frameworks enable parallel task execution, which can significantly reduce latency in complex workflows. Their modular structure simplifies adding or removing capabilities, providing flexibility. Additionally, multiple agents can cross-verify results, catching mistakes or inconsistencies. However, understanding these strengths clearly also prepares us to explore the inherent limitations and complexities that multi-agent frameworks introduce. More agents can mean more problems While multi-agent systems have clear benefits, managing these frameworks comes with unique challenges. At the core of these challenges is communication overhead. Agents must constantly exchange information, interpret messages, and act based on their interactions. Similar to human teams, any misunderstanding or missed detail can lead to duplicated efforts or errors. This constant communication often results in information fragmentation. Since each agent focuses on its specific role, no single agent has a complete view of the entire task. Key information can be lost or misinterpreted as it moves between agents, potentially leading to suboptimal outcomes. Managing communication and fragmented information significantly increases system complexity. Coordinating agents involves careful orchestration of roles, tasks, and execution sequences. When problems arise,... --- ### HALP: Maisa’s breakthrough in delivering reliability for enterprise automation > HALP lets AI learn from real work, not datasets. Digital Workers absorb business logic through live feedback, delivering reliable, traceable automation. - Published: 2025-06-04 - Modified: 2025-06-04 - URL: https://maisa.ai/agentic-insights/halp/ - Translation Priorities: Optional AI has made headlines for its potential to transform work, but inside most organizations, turning that potential into reliable automation remains a challenge. Business teams aren’t looking for impressive demos or clever assistants. They need AI systems they can trust to follow business logic, respect context, and stay consistent as things evolve. Yet the methods used to build these systems today often work against that goal. What if reliability didn’t depend on perfect data or complex training pipelines? What if AI could learn by doing, through real tasks and real feedback, inside the business itself? The limits of training methods for enterprises Human-in-the-loop (HITL) methods are used to make AI systems more accurate and aligned with human expectations. They rely on human feedback such as labeled examples, corrections, and supervision to teach models how to behave. This approach has been key to training today’s most advanced language models. Systems like GPT and Claude were refined through large-scale HITL processes, helping them perform well across a wide range of generic tasks. But when it comes to enterprise use, this method starts to show its limits. Business processes are specific, tools are unique, and rules change often. Applying HITL in this context means building custom datasets, coordinating technical teams, and retraining models just to keep systems functional. It is slow, expensive, and difficult to scale. For teams that need automation to adapt with the business, this approach becomes a bottleneck. Business logic should not have to wait for model retraining. Human-Augmented LLM Processing (HALP). A new way to teach AI What if AI could learn through real work, just like a new team member? HALP changes how we build reliable systems. Instead of relying on retraining cycles or complex setup, it enables AI to learn by doing. HALP stands for Human-Augmented LLM Processing, and it powers Digital Workers that learn directly from the way work happens. Configuring a Digital Worker through natural language Teams explain the task, walk through the logic, and share the tools they use. The system picks up that knowledge through natural interaction, without prompt engineering or rigid rules. Unlike traditional methods, HALP doesn't require labeled datasets or offline feedback loops. The learning happens in context, during real tasks. The system stays aligned with how the business actually works, even as things evolve. The reliability enterprises have been missing HALP unlocks what enterprise automation has long lacked: reliability in real work. Fast setup with less effort Digital Workers don’t need large datasets or precise prompts. They start from natural interaction and real context. Teams can build and adjust them without relying on IT or external consultants. Lower cost to launch and maintain Less time is spent configuring, correcting, or integrating. Business users can stay involved, reducing handoffs and rework. Scales across teams and processes Digital Workers adapt to different workflows. Logic can be reused, updated, and shared as the business evolves. Trust built into every step Each decision is traceable to a rule or piece of business logic. There... --- ### How fast is fast? A simpler way to deploy AI automation > A simpler way to deploy AI automation: define goals in plain language, let Digital Workers build tasks, reducing integration from months to weeks. - Published: 2025-06-03 - Modified: 2025-06-03 - URL: https://maisa.ai/agentic-insights/fast-ai-automation-deployment/ - Translation Priorities: Optional "How long will this take to integrate? " isn't just a casual question, it's a sign of deeper frustration and hidden technical challenges. Deploying enterprise AI or automation typically involves months of unexpected complexity and delay. Imagine instead if you could explain your goals clearly, just like briefing a colleague, and have your system ready to use. There's a simpler and more intuitive approach available. The reality behind AI rollouts AI promises speed, transformation, and effortless automation. But in practice, deploying these systems is anything but fast. Technical teams run into fragmentation early on. Tools don’t connect. Data lives in silos. Each step requires another handoff between departments, dragging timelines and creating room for error. Custom integrations are the norm, not the exception. Even small changes can trigger long rebuilds. Add compliance and oversight on top, and momentum stalls. At the same time, business teams are often stuck trying to translate their needs into detailed specifications for developers, hoping the intent carries through. But delivery is often out of sync with the original need. By the time the custom solution is built, priorities may have shifted or the need has evolved. The gap between what was asked and what gets delivered creates friction before anything goes live. And that’s just the infrastructure. AI itself introduces new friction: models that behave unpredictably, outputs that can’t be explained, and systems that are hard to trust in production. Without reliability and transparency, it’s risky to automate critical work. It all adds up to long timelines, slow iteration, and teams that are always waiting on something or someone to move forward. Natural language in, reliable automation out After all the delays and technical hurdles, it’s easy to forget what automation was supposed to feel like: fast, clear, and manageable. That’s where a different kind of approach comes in. What if instead of starting with architecture specs and tools, you start with the objective you’re trying to solve? That’s the shift behind Maisa Studio. In Maisa, you describe the outcome you want in plain language. A Digital Worker takes that input and begins building the automation, like onboarding a new teammate. You explain the goal, the context, and how it should behave. No diagrams, flowcharts, or technical specs. From that intent, the system generates a code execution map: deterministic, reliable, and fully explainable. You can follow every step, see exactly what’s happening, and trust the results. These Digital Workers are not generic bots. They learn how your organization actually operates, including the informal ways work gets done. They adapt through real usage, without needing massive datasets or manual tuning. They also connect with your tools out of the box, avoiding the usual friction of custom pipelines. And because the system understands goals and context, you don’t need to script every exception. You stay focused on what matters, it handles the details. How it works in practice Getting started with a Digital Worker is simple. You describe the task in plain language: what needs to be... --- ### Why we built Maisa this way: scientific proof we're on the right track - Published: 2025-04-24 - Modified: 2025-04-24 - URL: https://maisa.ai/agentic-insights/science-behind-maisa-architecture/ - Translation Priorities: Optional The architecture behind Maisa is the result of deliberate choices informed by research. A growing body of work has made it clear: while large language models offer impressive generative power, they fall short in several critical areas when used in isolation. Maisa’s strategic design responds directly to those gaps. Below is an overview of how each component is supported by scientific insight. Bridging reasoning and execution 📚 ReAct: Synergizing Reasoning and Acting in Language Models ReAct remains one of the most important foundations in the evolution of agentic AI. It introduced a core loop: reason, act, observe and repeat. This core helped reframe LLMs as active decision-makers rather than passive responders. This concept triggered the shift toward treating AI systems as agents capable of planning, adapting, and executing tasks in dynamic environments. While it's widely implemented today, its influence remains central to the architecture of AI systems designed for real-world decision-making. 📚 Hallucination is Inevitable: An Innate Limitation of Large Language Models LLMs are prone to fluent but inaccurate output. This limitation stems from architecture, not data. 📚 Steering LLMs Between Code Execution and Textual Reasoning 📚 Executable Code Actions Elicit Better LLM Agents 📚 Code to Think, Think to Code 📚 Chain of Code: Reasoning with a Language Model-Augmented Code Emulator These studies confirm the advantage of pairing LLMs with code execution: performance improves through verifiable logic, runtime validation, and structured task decomposition. While visible reasoning chains can appear coherent, they often mask logical gaps. Reliability increases when reasoning is grounded in execution, where each step is tested, not just described. 📚 Chain-of-Thought Reasoning in the Wild Is Not Always Faithful In fact, this other paper highlights and confirms that exposing reasoning chains through techniques like Chain-of-Thought prompting does not ensure factual accuracy. The presence of a detailed explanation can create a false sense of confidence, even when the underlying logic is flawed or unsupported. The model may appear to reason more deeply, but the steps often serve as post-hoc rationalizations rather than evidence-based logic. This distinction is critical: coherence doesn’t equal truth. Executable validation remains essential for ensuring that each step reflects actual reasoning. How this shapes Maisa: The research outlined in these papers affirms a path we had already taken. Each finding reinforces architectural choices we made early on. Confirming that the principles behind Maisa’s design are supported by emerging scientific consensus and designed to operate under real-world enterprise conditions. At the core is a reasoning engine structured around iterative decision-making loops, where each action is informed by observation and continuously adjusted until a defined goal is met. Instead of following fixed instructions, the system adapts continuously as conditions change and new inputs emerge. To support this, Maisa integrates a live code interpreter within the reasoning process, enabling the system to test assumptions, validate outcomes, and apply logical operations as part of its workflow. Rather than relying on text-based reasoning alone, every step can be executed, verified, and corrected in real time. Code is fundamental, not an... --- ### Digital Workers: bringing accountability to AI agency - Published: 2025-04-15 - Modified: 2025-04-15 - URL: https://maisa.ai/agentic-insights/digital-workers/ - Translation Priorities: Optional We keep hearing the term "AI Agents" everywhere these days. LinkedIn is flooded with posts about them. Tech conferences can't stop talking about them. Every startup pitch seems to include them. And yet, when we ask ten different people what an "AI Agent" actually is, we get ten completely different answers. This isn't merely semantics. It's a problem with deep historical roots. The concept of "agency" has challenged thinkers since ancient times. Aristotle explored it through his notion of "efficient cause", what initiates action. Plato examined it through questions of intention and purpose. The Stoics considered the relationship between individual action and natural order. These ancient philosophers recognized something we're rediscovering today: defining agency is inherently complex. It touches on intention, autonomy, purpose, and responsibility, concepts that resist simple definition. The "AI Agent" marketing landscape Let's be candid: the term "AI Agent" has become almost meaningless. Some vendors apply the label to sophisticated chatbots. Others use it for basic automation with a thin AI veneer. The enthusiasm has outpaced the clarity. We've sat through countless demos where the presenter uses "autonomous agent" language, but what they're showing is just a rigid workflow with some LLM responses inserted at key points. Meanwhile, back in the real world, teams are drowning in mundane tasks. How many hours did your organization lose last month to spreadsheet updates? Data entry? Report generation? Following up on routine processes? Likely more than anyone would prefer to acknowledge. All that time could have been invested in the work that actually moves the needle: creative problem-solving, relationship building and strategic thinking. What we actually mean when we say "agent" When we talk about a true AI Agent, we're talking about something specific: a system where the AI model itself (usually an LLM) actively manages its own workflow. It makes decisions about what to do next based on the current situation, not just following pre-programmed steps. It's like the difference between a simple calculator and a financial advisor. The calculator performs operations you explicitly request, while the advisor analyzes your situation, considers multiple factors, and recommends appropriate actions to achieve your goals. What's commonly marketed as "agents" today are often just "LLMs with tool access. " This reductive approach is like defining a human as "a body with hands. " Such simplistic definitions miss the essence of true agency: independent judgment, adaptation, and purposeful action. A genuine AI agent needs to be much more than just an LLM that can call functions. The missing piece: accountability Here's what concerns us most: as companies rush to deploy these so-called "agents," we're creating an accountability vacuum. We recently spoke with a banking executive who perfectly summed up the problem: "I can't hand over loan decisions to a system that can't explain itself. " She's absolutely right. In business, we need systems that are: Predictably reliable day in, day out Completely transparent about their actions and decisions Naturally integrated with how our teams already work We wouldn't trust our mortgage approval to... --- ### What happens when AI forgets? Context windows and their limits - Published: 2025-04-14 - Modified: 2025-04-23 - URL: https://maisa.ai/agentic-insights/ai-context-limitations/ - Translation Priorities: Optional AI can write emails, summarize research, or help you brainstorm ideas. It feels smart and useful. But for any of that to work, the AI needs context—the right information, at the right time, to generate a relevant output. The context window is the space where the model processes information. It holds the relevant details the user provides—what the model needs to understand and complete a task. Like short-term memory, it’s limited. If too much information is added, parts can be pushed out or forgotten, which affects the quality of the output. Context windows play a key role in how language models operate, but they come with built-in limits. Like us, these models have a limited attention span. And just like us, when they lose track of key details, their output can go off course. Let’s look at how this works—and why it matters. What is a context window? (and why it matters) Think of a context window like a whiteboard. It’s where the AI writes down what it needs to focus on—a mix of your instructions, the task at hand, and anything it has said before. But space is limited. Once the board is full, something has to be erased to make room for new information. Technically, the context window is the maximum amount of text the model can process at once. These chunks of text are called tokens. Tokens are chunks of words, and every input or output takes up space. If there’s too much to fit, earlier parts get cut off. You’ve probably noticed it before without realizing. Maybe ChatGPT forgets part of your request in a long conversation. Or it misses key details when analyzing a document. That’s usually a context limit. Why does this matter? Because even the smartest AI can’t give good answers without the right context. In business use cases like generating reports, handling customer queries, or reviewing contracts,the model’s output is only as good as what it can see. If the context is incomplete, the results will be too. The real challenge: attention, not just size Context windows have grown dramatically. For instance, models like Gemini Pro can handle contexts as large as 2 million tokens—that's about 5,000 pages of academic content at roughly 300 words per page. With context windows becoming so large, capacity is no longer the main challenge. The real challenge is how these models pay attention. AI language models use attention mechanisms, which determine what parts of the input to focus on. However, recent research like the paper "Lost in the Middle" shows that these mechanisms have a "U shaped" attention pattern—performance is strongest at the beginning and end of the context window, with a noticeable drop in the middle. This means information located centrally in a large context may be processed less effectively. Changing the position of relevant information within an LLM's input context affects performance in a U-shaped curve. Models perform best when relevant information is placed at the very beginning (primacy bias) or end (recency bias),... --- ### Advancing our vision for Accountable AI together with Microsoft > Maisa joins Microsoft for Startups Founders Hub and becomes a Strategic Partner, advancing Accountable AI and expanding access to its Digital Workers via the Azure Marketplace. - Published: 2025-04-07 - Modified: 2025-04-07 - URL: https://maisa.ai/agentic-insights/microsoft-partnership/ - Translation Priorities: Optional We are excited to announce that Maisa has been selected by Microsoft to become part of the Microsoft for Startups Founders Hub and recognized as a Microsoft Strategic Partner. Pushing forward our vision for Digital Workers This partnership reinforces our commitment to developing Accountable AI and Digital Workers that automate complex business processes. By tapping into Microsoft's extensive Azure infrastructure and specialized resources, Maisa gains powerful new capabilities to enhance the reliability, traceability, and performance of our AI technology. Strengthening Accountable AI This collaboration with Microsoft supports our goal of building trustworthy, transparent AI solutions. We continue working to advance AI systems that companies can rely on to automate processes, delegating to accountable Digital Workers. --- ### Black Box AI. How can we trust what we can’t see? > Lack of transparency in black box AI models complicates business decision-making, regulatory adherence, and trust in outcomes derived from internal data. - Published: 2025-04-01 - Modified: 2025-04-01 - URL: https://maisa.ai/agentic-insights/black-box-ai/ - Translation Priorities: Optional Artificial Intelligence is transforming critical decisions that affect businesses and people's lives, from approving loans and hiring candidates to medical diagnoses. Yet, many AI systems operate as "black boxes," providing outcomes without revealing how they were reached. This raises a fundamental question: how can we trust decisions made by systems whose reasoning we can't clearly understand? AI models learn from vast amounts of data, predicting outcomes without transparent, step-by-step logic. While their capabilities are impressive, this hidden reasoning creates uncertainty and potential risks. For businesses, relying on AI systems whose decisions are opaque can lead to serious accountability issues. If an AI makes a critical decision, how can companies confidently explain or justify it to employees, customers, or regulators? Addressing this trust gap isn't merely about compliance, it's about confidence and clarity in decision-making processes that shape real lives and business outcomes. Why is AI opaque? AI systems differ fundamentally from traditional software, which relies on clearly defined rules. Instead, AI learns directly from vast datasets. These models don’t have explicit instructions or human-understandable logic guiding their decisions. At their core, AI models use billions of interconnected parameters to convert inputs into outputs through complex mathematical calculations. This method is inherently probabilistic, meaning decisions are based on statistical patterns, not logical reasoning. With billions of these parameters adjusting simultaneously, tracking exactly how or why a specific output was produced becomes practically impossible. Unlike human decision-making, AI doesn't follow structured reasoning steps. It identifies correlations and patterns in data, predicting outcomes without explicit explanations. This absence of clear reasoning pathways means that decisions from AI systems often appear arbitrary, opaque, and difficult to interpret or justify. The risks of black-box AI in business Businesses rely increasingly on AI to automate important tasks, yet the opacity of these systems presents clear practical challenges. False confidence and AI hallucinations A major risk of opaque AI is "hallucinations," where AI produces seemingly accurate but entirely incorrect information. These false positives arise when the AI fills knowledge gaps or handles unclear inputs. For example, customer support chatbots might confidently provide false policy details, leading directly to confusion and complaints. Accountability gaps Opaque AI creates accountability issues. Traditional software clearly logs every decision step, making errors easy to track and correct. Black-box AI systems don't offer this clarity. When decision-making relies on hidden AI processes, identifying the exact point of failure becomes difficult, slowing corrections and process improvements. Legal and compliance risks Businesses must increasingly explain automated decisions clearly due to regulations like GDPR. If an AI-driven system, such as a credit scoring tool, makes decisions without understandable reasoning, businesses risk facing regulatory actions, customer complaints, or legal disputes. Uncertainty working with internal data and knowledge Businesses typically want AI to incorporate their specialized data and internal expertise clearly. However, black-box AI models obscure how proprietary business information is actually used. Without clear visibility, enterprises can't confirm that internal knowledge is applied correctly, risking inaccurate outcomes or impractical recommendations. Explainable AI (XAI) methods Several methods within... --- ### The AI Computer: overcoming fundamental AI challenges > The AI Computer marks a shift in computing, where AI moves beyond chatbots to orchestrate tasks, tools, and processes. - Published: 2025-03-05 - Modified: 2025-03-20 - URL: https://maisa.ai/agentic-insights/ai-computer-overcoming-ai-challenges/ - Translation Priorities: Optional Andrej Karpathy's LLM OS concept: envisioning large language models as the core of future operating systems Throughout history, humans have developed computational systems, formal logic, and scientific methods to verify our thinking. Can we do the same for AI? All technological revolutions required us to think outside the box, reimagine how we used to do things, and design new architectures and systems. AI is no different. We are now familiar with the power of AI, but every AI-native system lives in a chat-based interface. An instruction fine-tuned LLM so we can have conversations with this technology and help us through a wide array of tasks. However, in this configuration, we see some interesting add-ons. With ChatGPT, the most well-known AI-native system (a chatbot), the LLM uses some tools depending on the task: internet search, code executor, dalle for image generation, internal memory storage, and retrieval. Here we see an initial approach to the next computing paradigm, one where AI is not an assistant that lives in a chatbot interface but an orchestrator. Where is AI headed? how can we leverage the intelligence of this technology to reach another level? Looking at LLMs as chatbots is the same as looking at early computers as calculators. We're seeing an emergence of a whole new computing paradigm. Andrej Karpathy AI as an orchestrator Before operating systems, using a computer meant manually loading programs, writing commands in raw machine code or punch cards, and managing memory and processing power directly. Users had to allocate resources, track execution, and reload programs for each task. This made computing inefficient, time-consuming, and inaccessible to anyone without specialized technical expertise. Operating systems transformed computing by automating memory allocation, process management, and data storage. This software layer creates abstraction between users and hardware, eliminating the need to understand technical peculiarities. By allowing people to interact with computers at a higher level, operating systems made computing accessible and laid the foundation for personal technology, letting users focus on their tasks rather than the underlying mechanics. At the core of every operating system is the kernel, the component that coordinates processes and manages system resources. The AI Computer takes this further by making AI the kernel—the technology that orchestrates tasks, tools, and processes autonomously. Instead of merely executing predefined instructions, it becomes agentic, meaning it can independently make decisions and take actions to achieve a goal. A computer with agency Traditional computers operate on static, predefined logic—they execute commands exactly as programmed, following a structured set of rules to process user inputs. Every task requires a sequence of actions—clicks, keystrokes, and manual navigation—where the OS provides abstraction layers to help users manage files, applications, and processes more efficiently. However, the responsibility of executing tasks still lies entirely with the user. The AI Computer shifts this paradigm by embedding intelligence at its core. Instead of merely processing user commands, it understands objectives, interprets intent, and autonomously orchestrates the necessary steps to achieve the desired outcome. This represents a deeper level of abstraction—one... --- ### What is Agentic Process Automation? The next frontier of Intelligent Automation - Published: 2025-02-20 - Modified: 2025-06-12 - URL: https://maisa.ai/agentic-insights/what-is-agentic-process-automation/ - Translation Priorities: Optional At the core of every organization lies a foundation of business processes. While essential, these processes often involve mundane, repetitive tasks that take time and resources away from more valuable contributions. Humans have always sought ways to overcome repetitive work. The Industrial Revolution brought machines to handle physical labor. The rise of computers and spreadsheets transformed how we process information. Robotic Process Automation (RPA) marked the first major leap in digital automation, enabling software to handle repetitive tasks at scale. Yet today, businesses still find themselves flooded with mundane, manual work. Robotic Process Automation (RPA). The origins Robotic Process Automation (RPA) represented the first wave of digital automation by programming software robots to replicate human actions — clicking buttons, entering data, and moving information between systems using predefined rules. These software bots excel at executing repetitive sequences, reducing manual effort in rule-based workflows. Industry leaders like UiPath, Automation Anywhere, Blue Prism, and ServiceNow have helped enterprises automate large-scale structured workflows. The banking, financial services, and insurance (BFSI) sectors have been among the biggest adopters, as a large portion of their back-office operations rely on rigid, repetitive administrative tasks such as data entry and document processing. However, RPA hits a ceiling when confronted with tasks requiring cognitive skills or handling unstructured data—the kind of work that fills most knowledge workers' days. Agentic Process Automation (APA). The next frontier in Intelligent Automation Agentic Process Automation (APA) relies on AI-driven agents to handle business processes autonomously. These agents interpret context, learn continuously, and adapt their approach based on changing conditions. Their self-healing capabilities allow them to detect and resolve process issues automatically, adjusting workflows when problems arise. While traditional automation follows fixed rules, APA can tackle unpredictable tasks and make informed decisions, expanding automation into areas that require flexibility and judgment. APA systems focus on objectives rather than predefined steps. You specify the desired outcome, and the system creates its own execution plan, leveraging available tools and resources to complete the task. With the right tools and context, AI Agents can dynamically design and execute workflows based on natural language instructions and company guidelines, adapting in real-time without relying on fixed instructions. Benefits of Agentic Process Automation Self-Healing: Detects and corrects errors autonomously, minimizing downtime and reducing maintenance efforts. Adaptability: Handles open-ended tasks, changing conditions, and unexpected scenarios. Intelligence: Goes beyond rule-based execution by reasoning over unstructured data, making informed decisions, and generating insights. Cost-Effective: Requires fewer resources and less time to build, deploy, and maintain than traditional automation systems. The challenge with Intelligent Automation The core of agentic systems is to reduce manual intervention, but full autonomy is not practical for every use case. Ensuring reliability requires the right guardrails, allowing effective collaboration between humans and technology. This balance is essential for maintaining control and trust as AI agents take on more complex workflows. Then we face the opaqueness and reliability challenges of AI systems. Built on probabilistic models, they provide limited visibility into decisions while being prone to hallucinations and inconsistent... --- ### Making AI accountable: Maisa raises pre-seed round - Published: 2024-12-25 - Modified: 2025-03-18 - URL: https://maisa.ai/agentic-insights/maisa-raises-pre-seed-round/ - Translation Priorities: Optional Back in March, we introduced the first version of the KPU, setting new benchmarks that surpassed leading models. Since then, our technology has advanced with the launch of the Vinci KPU, our team has grown, and we’ve welcomed our first customers. Yet, the core challenge in the AI market remains unchanged: a persistent lack of accountability, reliability and transparency in AI systems. Manu Romero & David Villalón The trust problem in AI Generative AI lacks accountability. Techniques like Chain-of-Thought reasoning, RAGs, and multiagent systems aim to address more sophisticated challenges but still rely on probabilistic predictions, not deterministic computations. AI is unlocking opportunities across countless domains, but the complex world of business demands greater accountability. Mission-critical tasks require not only answers but traceable, evidence-based processes to reach them. Without these, we risk hallucinations—fabricated outputs that render AI results unreliable. This lack of trustworthiness is why fewer than 6% of corporations use AI for anything beyond basic tasks like question-and-answer bots. What we are building We believe the solution isn’t in refining existing approaches but in creating a new kind of computing system—one that combines AI's creative problem-solving with the determinism of traditional computational systems. With Maisa, you can create bulletproof AI Agents. These are a new generation of Digital Workers that follow natural language instructions to achieve specific outcomes and goals, making intelligent and reliable automation a reality. With the best behind us We are fortunate to be backed by visionary investors committed to advancing our mission. Our pre-seed round brought in $5 million, led by NFX and joined by Village Global, the venture fund backed by Mark Zuckerberg, Eric Schmidt, and Jeff Bezos. This funding was further supported by Sequoia's Scout Fund ,and DeepMind PM Lukas Haas, and other angel investors. This funding enables us to continue developing our product and expanding our research initiatives. We’re also genuinely grateful for the recognition our work has received, including a recent feature in Forbes that highlights this important milestone for us. Maisa is going to be a major player in Agentic Process Automation (APA) helping businesses across the world transform their core, business-critical functions through AI. It will allow them to work faster, more efficiently and achieve new and radical ways of operating. Anna Piñol, NFX David and the Maisa team are building a transformative technology to turn AI agents into actual workers that are capable of reasoning through complex workflows. We're super thrilled to be a part of their journey and are very excited to see the new benchmarks and enterprise traction Max Kilberg, Village Global Come join us If you are interested in joining our mission of making AI accountable, visit our careers page #page_section_71 a{font-size:17px ! important;}#page_section_71 p{line-height:27px ! important;font-size:17px ! important;} --- ### Introducing Vinci KPU > Comprehensive overview of Maisa AI's Vinci KPU. Read detailed benchmark comparisons, architecture improvements, and new features. Includes performance analysis on GPQA, MATH, HumanEval, and ProcBenc. - Published: 2024-11-26 - Modified: 2025-02-07 - URL: https://maisa.ai/agentic-insights/vinci-kpu/ - Translation Priorities: Optional Introduction On March 14, 2024, at Maisa AI, we announced our AI system to the world, enabling users to build AI/LLM-based solutions without worrying about the inherent issues of these models (such as hallucinations, being up-to-date, or context window constraints) thanks to our innovative architecture known as the Knowledge Processing Unit (KPU). In addition to user feedback, the benchmarks on which we evaluated our system demonstrated its power, achieving state-of-the-art results in several of them, such as MATH, GSM8k, DROP, and BBH— in some cases, clearly surpassing the top LLMs of the time. Vinci KPU Since March, we have been proactively addressing inference-time compute limitations and scalability requirements, paving the way for seamless integration with tools and continuous learning. Today, we are excited to announce that we have evolved the project we launched in March and are pleased to present the second version of our KPU, known as Vinci KPU. This version matches and even surpasses leading LLMs, such as the new Claude Sonnet 3. 5 and OpenAI’s o1, on challenging benchmarks like GPQA Diamond, MATH, HumanEval, and ProcBench. What’s new on the Vinci KPU (v2)? Before discussing the updates in v2, let’s do a quick recap of the v1 architecture. KPU OS Architecture Our architecture consists of three main components: the Reasoning Engine, which orchestrates the system's problem-solving capabilities; the Execution Engine, which processes and executes instructions; and the Virtual Context Window, which manages information flow and memory. In this second version, we've made significant improvements across all components: Reasoning Engine Improvement: We have enhanced the KPU kernel, furthering our commitment to positioning the LLM as the intelligent core of our OS Architecture. This advancement allows for more sophisticated reasoning and better orchestration of system components. Execution Engine Enhancements: We have successfully integrated cutting-edge test-time compute techniques and made the execution engine more robust, secure, and scalable. This ensures reliable performance while maintaining high-security standards for tool integration and external connections. Virtual Context Window Refinements: We have refined our Virtual Context Window through improved metadata creation and LLM-friendly indexing. This enhancement optimizes how information flows through the system and lays the groundwork for unlimited context and continuous learning capabilities. KPU Architecture Benefits What makes these results particularly significant is that they're achieved by our KPU OS, acting as a reasoning engine, which focuses on understanding the path to solutions rather than providing answers. As main benefits, we can highlight: Model Agnostic Architecture (Better base models, better performance) Full multi-step traceability: configurable observability: Debug mode, visual representation, et. al. Provides better human-in-the-loop and over-the-loop control. Mitigate, almost fully eliminates, hallucinations: While this approach minimizes AI-generated inaccuracies, it may still encounter issues like errors in tool execution, incorrect data sources, or suboptimal approaches to solving the problem. Lower Latency to resolve problems than other systems in the market. Cost-efficient (up to 40x times cheaper than RAG, reasoning engines and Large Reasoning Models). Fully flexible and customizable with out-of-the-box functionalities: Unstructured data management, tools integrations, data processing... Autonomous execution with self-recovery/self-healing. It... --- ### Hello world - Published: 2024-03-15 - Modified: 2025-01-09 - URL: https://maisa.ai/agentic-insights/hello-world/ - Translation Priorities: Optional Hello World In recent periods, the community has observed an almost exponential enhancement in the proficiency of Artificial Intelligence, notably in Large Language Models (LLMs) and Vision-Language Models (VLMs). The application of diverse pre-training methodologies to Transformer-based architectures utilizing extensive and superior quality datasets, followed by meticulous fine-tuning during both Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback/Reinforcement Learning with Augmented Imitation Feedback (RLHF/RLAIF) stages, has culminated in the development of models. These models not only achieve superior performance metrics across various benchmarks but also provide substantial utility in everyday applications for individuals, encompassing both conversational interfaces and API-driven services. These language models, based on that architecture, have several innate/inherent problems that persist no matter how much they advance their reasoning capacity or the number of tokens they can work with. Hallucinations. When a query is given to an LLM, the veracity of the response cannot be 100% guaranteed, no matter how many billions of parameters the model in question has. This is due to the intrinsic token-generating nature of the model, that generates the most likely token, which might not be a factual reason for the response to be trustable . Context limit. Lately, more models are appearing that are capable of handling more tokens, but we must wonder: at what cost? The "Attention" mechanism of the Transformer Architecture has a quadratic spatio-temporal complexity. This implies that as the information sequence we wish to analyze grows, both the processing time and memory demand increase proportionally. Not to mention the problems that arise with this type of model, such as the famous "Lost in the middle" , which means that sometimes the model is unable to retrieve key information if it is "in the middle" within that context. Up-to-date. The pre-training phase of an LLM inherently limits the data up to a certain date. This limitation affects the model's ability to provide current information. Asking the model about events or developments after its pre-training period may lead to inaccurate responses, unless external mechanisms are used to update or supplement the model's knowledge base. Limited capability to interact with “digital world”. LLMs are fundamentally language-based systems, lacking the ability to connect with external services. This limitation can pose challenges in tackling complex problems, as they have restricted abilities to interact with files, APIs, systems, or other external software. Architectural Overview The architecture we have named KPU (Knowledge Processing Unit) has the following main components. Reasoning Engine. It is the "brain" of the KPU, which orchestrates a step-by-step plan to solve the user's task. To design the plan, it relies on an LLM or VLM and available tools. The LLM is plug-and-play, currently extensively tested with GPT-4 Turbo. Execution Engine. Receives commands from the Reasoning Engine which it executes and whose result is sent back to the Reasoning Engine as feedback for re-planning. Virtual Context Window. It manages the input and output of data and information between the Reasoning engine and the Execution engine, ensuring that information arrives at the Reasoning... --- ---