Why evaluations matter for AI agents

Build agents, evaluate them, and scale with confidence

If you’re building AI agents, whether coded or low-code, you know guesswork doesn't scale. You need a reliable, repeatable way to measure performance, catch regressions, and improve quality. That’s where evaluation driven development comes in.

When you’re designing your agent in evaluation mode, you can move from “this sometimes works” to “this works consistently.” Evaluation-driven development gives you clarity, control, and confidence, without slowing you down. Here’s what that means, how it works under the hood, and why it matters.

The evaluator-centric model

Evaluations are how you measure agent quality. They use observability data (like traces, reasoning steps, and outputs) to assess performance in specific scenarios. This helps you pinpoint where things break down and improves reliability. Put simply: if you’re unsure whether to update the prompt, tweak the workflow logic, or switch LLMs, evaluations give you clarity. No guesswork, no blind trial-and-error.

The idea behind evaluations is to expose agents to challenging conditions—like scenarios with messy, incomplete, or ambiguous inputs—to see how well they hold up. While your agent may perform reliably in expected situations, evaluations help you see if your agent can adapt and stay on track when things get more difficult.

At the core of the evaluation framework are evaluators: modular, reusable scoring units that assess how well your agent performs a given task. Evaluators look at your agent’s output or your entire agent’s trajectory.

You decide what “good” looks like. Evaluators do the grading. Evaluators can be deterministic or LLM-based:

1. Use deterministic evaluators when the output is specific and predictable.

• Like booleans (false or true), exact strings, alphanumeric values, or arrays of primitives for exact matches.

• Or like JSON similarity checks, which compare structure and content between JSON objects.

2. Use LLM evaluators when the output is fluid or open-ended.

• These use an LLM to judge how close the agent’s response is to an expected, acceptable answer.

You’ll get pre-built evaluators for every new agent you build in our cloud platform. The pre-packed evaluators are output correctness and trajectory coherence. These are usually enough to start building confidence that your agent is behaving as expected.

As you get closer to production, you may want to go deeper. Depending on your use case, you can create custom evaluators to better understand how your agent is performing.

Still unsure which evaluator to use for your agent? Check out our best practices guide or ask the community.

Evals are important…Without structured evals, development becomes guesswork. Mastering AI evals means building smarter, faster, and more reliable agents. —Andrew Ng, founder of DeepLearning.AI

Evaluation-driven development

You can't evaluate what you can't observe. That's why agent observability is built into the UiPath Platform. When an agent takes an action, those steps are tracked and shown to you (the traces, the reasoning steps, and outputs). You’ll see this data when you're designing and running the agent and after you publish it and it's operating on its own. You'll see how your agent plans, which tools it uses, and what outputs it generates, so you can trace back to where things go right or wrong in its process.

Agent observability helps you narrow down the root causes of poor agent performance. This is how you move from “the agent didn’t work” to “the tool it picked was wrong” or “the summarization step lost key details.”

Here’s an example:

Say you’ve built a research agent. It finds sources, collects content, summarizes results, and iterates if needed. You can:

• Evaluate the final output 

• Evaluate each decision along the way (like whether it picked the right tool at each step)

• Measure how often it loops, repeats steps, or escalates unnecessarily

When your agent isn’t performing as expected, your first instinct will likely be to use the built-in LLM to troubleshoot. And that’ll often work. You can tweak the prompt, change the model, swap out a tool, narrow the agent’s scope, or a combination of all these techniques, and you’ll probably get a quick fix.

This approach works well for one off scenarios. However, you have to prompt the LLM manually to get suggestions. It's a reactive method and only covers the scenarios you can think of. You won’t catch the edge cases your agent hasn’t encountered yet.

This is where evaluations come in. By running your agent across a wide set of varied scenarios, evals simulate harsh, unpredictable conditions. They reveal where your agent breaks down so you can reinforce it before those issues hit production. Read on to learn how to set up evaluations for your agents.

Evaluations for low-code agents are now available in the cloud. Build your first agent and evals here.

Subscribe to our release notifications to learn first when simulations or evaluations for coded agents are available to the general public here.

Simulations and trajectory evaluations as your foundation to scale for low-code agents and coded agents

You can start by running evaluations with simulated data and mock tools. It's fast, cheap, and good for iterating. Use simulated tools especially when your real tools aren't ready yet (maybe you're missing credentials or waiting for internal approvals) or when the real tools would make changes to external systems. With mocked tools, you won't end up with a bunch of test content you have to clean up later.

As you gain confidence, you can switch from simulated data and mock tools to real data in order to get more granular. One way to create evaluations is from actual test runs in design time. Take a few agent runs in design time as samples, curate them, and start tracking how future changes affect performance. But building every evaluation like this is time-consuming, and running every eval with live data and real tool calls gets expensive, especially if you’re testing a lot of variations. That’s where simulations come in, and it’s a good idea to start with these first.

We’re adding simulation capabilities to UiPath agents that let you run your agent against synthetic data, as well as mock tools that will save on time and cost. 

You’ll be able to test edge cases, rare errors, or long workflows without triggering actual actions or using real inputs. Simulations will be especially useful when building larger evaluation datasets, when testing at scale, and at the beginning of agent setup before you run evaluations with real data.

Manage your evals in design and run time

Start creating eval sets in design time, but don’t stop there. Once your agent is live, it will generate production runs. If it fails in a real scenario, use that as signal and create new evals to cover that case.

Just rate the run, leave feedback, and either add the issue to an existing eval set (if you’ve worked on it before) or start a new one for fresh issues. Then simulate more scenarios around that case.

If you’re managing multiple agents doing similar jobs, you can share evals across them using our platform’s import/export features.

Whether you’re just starting out or scaling agent-based apps, our goal is to make this process easier, cheaper, and more reliable, without slowing you down. The key takeaway: evals are critical infrastructure for trust.

These posts aren’t just about how to use the tools, they’re about how to think about evals. Subscribe to get more posts like this and learn how evals evolve and help you build more trustworthy agents.

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