• AI agents require a different approach than traditional RPA. They can reason and act autonomously, making visibility into their behaviour more important.
• Measurability is essential for trust. Monitor not only whether a process succeeds, but also how an agent performs and where deviations occur.
• Governance and human-in-the-loop make autonomy responsible. Define where an agent can act independently and when human intervention is required.
• Monitoring helps agents improve continuously. By analysing where agents perform well and where people need to step in, you can make targeted improvements.
• UiPath brings agents, robots and people together. This creates an environment where organisations can deploy and scale agentic automation with greater control.
• The goal is not maximum autonomy, but responsible autonomy. The more you delegate to an agent, the more important it becomes to understand what it is doing.

Interest in agentic automation is growing rapidly. Organisations are discovering that AI agents can do more than process information. They can reason, make decisions and take action within business processes.

This creates a new form of automation. Whereas traditional RPA largely relies on predefined rules and steps, AI agents can handle more complex situations and different types of input.

That creates significant opportunities. But it also raises an important question:

How do you know that an AI agent is doing what it is supposed to do?

The more autonomy you give an agent, the more important it becomes to make its behaviour visible and manageable.

From automation to autonomous action

With traditional RPA, you generally know what a robot is going to do. The process is defined in advance and the robot follows the configured steps.

An AI agent works differently. It can interpret context, combine information from different sources and determine which action to take.

That makes agents particularly interesting for processes that are difficult to capture entirely in fixed rules. At the same time, it means you need to look beyond the outcome of a process.

You want to know:

• Why did the agent make this decision?
• How often does it successfully handle a process on its own?
• When is human intervention required?
• Where do errors or exceptions occur?
• Does the agent’s behaviour change over time?

That is why AI agent monitoring is not an additional layer you add afterwards. It is part of how you design and deploy an agent.

AI agents need to be measurable

An AI agent that operates autonomously needs to be monitored and assessed.

That starts with visibility into performance. Which processes are being handled successfully? Where does the agent struggle? How often does a human need to intervene? And what happens when the agent encounters a situation it has not seen before?

UiPath is increasingly building these capabilities into its platform. Within the UiPath Agent environment, organisations can monitor deployed agents, review performance and investigate incidents or degraded performance.

This matters because managing an agent is fundamentally different from managing a traditional robot.

With a robot, you might look at successful runs and error messages. With an agent, you also need to assess whether it reached the right outcome and stayed within the boundaries you defined.

Four questions you need to be able to answer

At Ciphix, we therefore look beyond the implementation of an AI agent. We want to understand from the outset how an agent’s behaviour can be monitored, assessed and improved in practice.

Four questions consistently come up.

1. Is the agent still working as intended?

An agent does not always receive exactly the same input and does not necessarily follow the same route through a process.

That makes continuous performance monitoring important. Not only to identify errors, but also to detect changes in behaviour.

A declining performance score, more exceptions or an increasing need for human intervention can all be reasons to review how an agent is configured.

2. What exactly is the agent doing?

Autonomy should not mean that behaviour becomes invisible.

People responsible for a process need to understand what an agent is doing and where it reaches its limits. That does not mean everyone needs to see every technical step.

It means having the right level of visibility: which decisions is the agent making, which actions is it taking and what is the outcome?

UiPath positions observability and auditability as part of the governance surrounding agents. The platform can capture agent actions, prompts, responses, tool calls and human approvals, providing insight into how agents operate.

3. What happens when something goes wrong?

Not every situation needs to be handled fully autonomously.

For certain decisions, you may want a human to review or approve an action. Human-in-the-loop makes it possible to build human judgement into the process where it matters.

Governance is equally important. You need to define what an agent can and cannot do and be able to enforce those boundaries during execution.

UiPath supports this through governance policies and its AI Trust Layer, helping organisations determine which agents can be used and under what conditions they can operate.

Governance is therefore not a barrier to automation. It is what allows you to introduce autonomy within clear boundaries.

4. Where can the agent improve?

Measurability is not only about control. It also provides the information you need to improve an agent.

Imagine an agent successfully handles most requests on its own, but regularly needs human support for a specific type of request. That is valuable information.

Perhaps the agent is missing context. Perhaps it struggles to interpret a particular situation. Or perhaps the process itself could be designed more effectively.

This creates a continuous improvement loop:

measure → analyse → refine → measure again.

Monitoring then becomes more than a safety net. It becomes a way to continuously improve the performance of your AI agents.

Why UiPath is interesting in this context

For organisations already working with automation, moving towards agentic automation is not simply a matter of adding a new AI tool.

The real challenge is bringing agents, robots, APIs and people together within the same business process.

This is where the UiPath platform becomes particularly relevant. UiPath positions its platform as an environment where agents can reason within defined boundaries, robots can perform deterministic tasks, APIs can connect systems and people can step in when human judgement is required.

This makes it possible to combine different forms of automation rather than expecting an agent to do everything.

An agent does not have to take over the entire process.

The combination can be much more powerful: the agent determines what needs to happen, the robot performs fixed actions, systems provide the necessary data and a human steps in when judgement is required.

Governance is not something that needs to be added afterwards. UiPath treats governance, observability and human-in-the-loop as part of the platform approach to agentic automation.

From AI experiment to reliable automation

Building an AI agent is no longer the hardest part.

The real challenge begins when you make that agent part of a business process.

You need to know how it performs. You need to identify deviations. You need to determine when a human should intervene. And you need to be able to understand what actions an agent has taken.

That is what separates an interesting AI pilot from agentic automation that can actually be used in production.

At Ciphix, we help organisations not only implement UiPath and AI agents, but also design the processes around them: how agents fit into existing automation, where human-in-the-loop is needed and how monitoring and governance can be built in from the start.

Because the more autonomy you give an agent, the more important it becomes to understand what is happening.

AI agents do not need to be completely predictable. But their behaviour does need to be visible, measurable and manageable.

That is the foundation for scaling agentic automation responsibly.

Jeroen Blok is a UiPath specialist at Ciphix. If you’re interested or have any questions, feel free to contact us at ciphix.io/contact