AI Agents in the enterprise: strategy for agentic automation

Companies today clearly recognize the value of AI. They’re investing heavily, launching pilots, and experimenting with models across various business functions. Yet, the reality remains: most AI initiatives never reach production, falling short of their intended impact.

The issue isn’t the technology itself. Instead, companies face operational challenges around reliability, integration, and accountability when embedding AI into everyday workflows. Traditional automation systems offer stability but lack flexibility, limiting their value to rigid, repetitive tasks. On the other hand, AI brings powerful reasoning capabilities but introduce uncertainty, including unpredictable behaviors or hallucinations.

Agentic Process Automation (APA) involves deploying AI agents that reason, act, and operate autonomously to automate business processes. Successfully adopting this approach, however, requires organizations to carefully consider how these agents will be governed, controlled, and integrated into existing operational structures.

Where strategy begins

Understanding the operational challenges is the starting point. Addressing these requires clear strategies focused on trust, autonomy, and visibility.

Trust, Reliability, and Guardrails

AI agents can perform complex tasks, but their outputs are not always predictable. They sometimes produce incorrect or misleading responses, a behavior known as hallucination. The acceptable level of accuracy depends heavily on the specific business scenario: while minor inaccuracies might be fine in a marketing draft, they are unacceptable in financial reporting or customer billing.

Because of these limitations, clear guardrails must be established. Guardrails are rules and constraints that keep AI agents working within defined limits. This includes setting clear boundaries on what actions agents can take, what data they can access, and how decisions are validated. Effective guardrails allow businesses to confidently deploy AI agents, knowing they’ll perform reliably within established boundaries.

Human–Agent Collaboration and Autonomy Tiers

For AI agents to be effective in real-world settings, it’s essential to clearly define how humans and AI agents collaborate. Autonomy isn’t binary; instead, the appropriate level of human involvement depends on the task, the reliability of the agent, and the risks involved. This collaboration typically falls into one of three categories:

• Human-in-the-loop: AI agents suggest actions, but people make final decisions. This approach suits tasks with high risks or significant business impact, such as approving financial transactions.
• Human-on-the-loop: AI agents act autonomously while people periodically review their decisions. This works best in tasks like content moderation or customer support, where errors have moderate impacts and occasional oversight is sufficient.
• Out-of-the-loop: AI agents handle tasks independently, within strict guidelines. Suitable for low-risk, repetitive tasks, such as simple data entry or report generation.

Choosing the right autonomy level ensures AI agents effectively support teams, enhancing productivity while maintaining accountability.

Visibility, Auditability, and Validation

AI agents often act like black boxes, performing complex actions without clearly revealing their internal reasoning. This lack of visibility can quickly undermine trust and control, especially when something goes wrong.

To confidently deploy AI agents in business processes, organizations need clear visibility into how these agents operate. This means maintaining transparent logs of every step the agent takes and every decision it makes. Additionally, businesses require straightforward validation mechanisms, such as human reviews or automated checks, to quickly confirm that the agents are acting correctly.

By embedding visibility and validation directly into these systems, organizations can ensure AI agents perform consistently, remain accountable, and align closely with business objectives.

Putting it into practice

Once you’ve clearly defined how AI agents will collaborate with your teams, the next step is translating this strategy into practical, testable initiatives. It starts small, with a pilot, then grows incrementally toward production.

Select a process and build the MVP

Start by choosing a specific business process where an AI agent can provide clear value. This process should be simple enough to manage yet meaningful enough to prove the agent’s capability. Good candidates typically have clearly defined tasks, measurable outcomes, and data readily available.

The goal of this initial stage is to create a minimum viable product (MVP), a basic version of the automation that can demonstrate tangible results. The MVP lets you test the agent’s ability to reliably complete tasks, validate outputs, and clearly understand where improvements might be needed.

Focusing on a small, well-defined process makes it easier to build trust, verify performance, and set the stage for broader deployment.

Scale Gradually, Layering Autonomy and Scope

After your initial pilot proves successful, you can begin expanding the automation to cover additional use cases. But scaling AI agents isn’t just about doing more, it’s about carefully increasing their autonomy and scope in a controlled way.

Before adding new tasks or higher autonomy levels, confirm three essential elements are firmly established:

• Effective monitoring: Make sure you have clear visibility and logging to quickly identify any issues.
• Human override paths: Ensure there are simple, reliable ways for people to intervene if needed.
• Reliable data: Confirm the data quality and consistency remain strong, ensuring agent decisions stay accurate.

When increasing autonomy, do it deliberately. Adjust guardrails intentionally, rather than letting them loosen by accident. Treat AI agents like software products: keep clear versioning, maintain regular reviews, and ensure you can track their behavior easily.

This structured approach allows your organization to confidently scale automation, achieving the desired business impact without compromising control.

A new way to work with AI

Agentic Process Automation represents more than just new technology. It’s about transforming how teams and systems collaborate. When designed intentionally, AI agents become dependable collaborators, managing structured tasks and freeing people to focus on strategic and creative work.

Yet, deploying AI effectively requires recognizing and addressing its inherent limitations, like unpredictability, potential errors, and the risk of limited visibility. Successful implementation means proactively building systems that operate transparently, remain accountable, and provide clear visibility into every decision.

At Maisa, we’re addressing these challenges by creating Digital Workers built with accountability at their core. By designing systems that reason clearly, act consistently, and explain their actions transparently, we help businesses confidently scale automation without sacrificing control.