1. AI Model
At the center is usually a language model. It’s what lets the agent understand a goal, break it into steps, and make decisions along the way. This is the reasoning engine. It plans, reacts, and adjusts based on the context.
AI Agents
AI Agents explained: what they are, how they work, and why they matter for automation.
AI agents are software systems that use AI to pursue goals and complete tasks.
AI is at the core of how agents work. It lets them understand goals, reason through tasks, and make decisions based on context. They don’t just follow instructions. They adapt to the context around them.
They also take action. AI agents can connect to tools and systems to do things like send an email or update a database.
The term AI agent is used in many ways today, and the market is full of noise. Some describe any system that includes AI in part of the process as an agent.
We define AI agents as systems with AI at their core—systems that are objective-driven, capable of reasoning, and able to decide how to accomplish tasks.
We’re all familiar with AI assistants. Tools like ChatGPT or Copilot help us write, summarize, or answer questions. They’re helpful, but they rely on us to guide them, one prompt at a time.
AI agents are a step further. They’re not just responding. They’re acting. Agents can make decisions, use tools, and complete tasks on their own, without being told what to do at every step.
| AI Agent | AI Assistant | |
|---|---|---|
| Purpose | Acts on its own to complete tasks and reach goals | Helps users by following instructions or prompts |
| Capabilities | Can handle complex tasks, make decisions, adapt, and learn over time | Provides answers, suggestions, or simple actions based on input |
| Interaction | Proactive and goal-driven | Reactive and prompt-based |
AI agents work by combining different parts that let them reason, act, and in some cases, learn from experience. The way these parts are used can vary depending on how the agent is designed.
At the center is usually a language model. It’s what lets the agent understand a goal, break it into steps, and make decisions along the way. This is the reasoning engine. It plans, reacts, and adjusts based on the context.
To take action, agents rely on tools. These can include APIs, databases, or other software. The model decides what to use and when, depending on the task. This is how agents move from planning to actually getting things done.
Some agents can store and recall past interactions. This memory helps them stay consistent, adapt to feedback, and improve over time. It turns them into systems that learn, not just repeat.
AI agents can do more than just respond to prompts. Their capabilities make them useful for handling complex tasks, adapting to new situations, and acting on their own.
Agents work toward goals without needing step-by-step instructions. Once given an objective, they decide how to move forward. They break tasks into smaller steps, sequence them, and adjust as things change. This includes handling edge cases or exceptions. Agents can use tools to complete tasks. They interact with systems, send messages, retrieve data, or trigger workflows. They can manage tasks that involve multiple steps, decisions, or tools—keeping track of what needs to happen and in what order. Some agents improve over time by storing past experiences, learning from feedback, or adapting to user behavior.
Autonomy
Planning
Action
Complexity
Learning
As powerful as AI agents can be, using them in real-world scenarios comes with challenges. These systems need to be reliable, understandable, and manageable, especially in business contexts where accuracy and trust matter.
AI models often behave like black boxes. It can be hard to see why an agent made a certain decision. Without traceability into the reasoning process, it’s difficult for teams to trust or verify the outcome.
Since agents often rely on language models, there’s a risk of them generating false or misleading information. This can impact accuracy and trust. Running agents at scale is complex. Their lack of traceability and reliance on AI outputs make it harder to manage errors and ensure reliability. Coordinating tools, tasks, and learning systems adds another layer of difficulty
Traceability
Hallucinations
Complexity
AI agents open up new ways to automate and scale work. But in practice, teams often struggle with control, reliability, and trust. That’s where digital workers come in.
Digital workers are a type of AI agent built specifically for business environments. They can manage full workflows from start to finish, with the kind of structure and visibility companies need.
What makes them different is their focus on traceability and accountability. Each step is visible. You can see what decisions were made, what tools were used, and why. That means fewer surprises and more trust.
They’re designed to handle real business tasks—coordinating across systems, adapting to changes, and keeping a clear record of everything they do. So instead of just experimenting with AI, teams can put it to work.
Start automating the impossible