Imagine you're handed the reins of a wild horse — untamed, immensely powerful, and brimming with potential. You wouldn’t gallop into a race immediately. First, you'd test its limits. You’d take it through forests and fields, over rocks and rivers. You’d understand how it behaves when tired, startled, or challenged. Only then would you even think of calling it race-ready.
Our recent session at Knowledge 2025 on "Taming the Blackbox – Crafting Agentic Evaluations," unpacked the state of AI agents and agentic workflows today, with the same analogy. Much like that wild horse, these systems are autonomous, powerful, and capable of surprising feats. But as with all emerging capabilities, they come with unpredictability — shaped by context, nudged by tools, and constrained only loosely by prompts.
And that’s precisely why evaluations are not a luxury. They are essential!
From One-Off Tests to Scalable Evaluations
Too often, teams test AI agents manually — feeding in one input, observing the output, tweaking, and repeating. This can help in early stages, but it’s like trying to understand a horse’s behavior by walking it down the same path repeatedly. It tells you nothing about how it will act on a mountain trail, in a thunderstorm, or when it sees a squirrel.
Agentic Evaluations scale this process. They simulate diverse scenarios — terrains, if you will — across a dataset of inputs. Instead of asking “Did this one input produce the right answer?”, we begin asking:
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How well does the agent perform to different variations?
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How consistently does it behave across these?
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Is the path it takes to solve a task transparent and logical — or just lucky?
This shift is transformative.
Evaluating Not Just What, But How
Agentic workflows are often non-deterministic. They may reach the same outcome through different reasoning paths — or reach different outcomes with superficially similar inputs. Simply looking at what they produce is insufficient.
That’s why our session emphasized the need to evaluate the “how.”
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Did the agent use tools sensibly?
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Was its reasoning interpretable?
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Did it make meaningful progress even if the final answer wasn’t perfect?
By capturing this nuance, evaluations move beyond binary judgments. They offer designers and developers a clear window into the black box — helping to refine not just performance, but trust.
Why This Matters More Than Ever
We all know, "with great power comes great responsibility". As AI agents begin to take on tasks that affect real users — from writing code, to making business recommendations — we owe it to ourselves (and our users) to be confident in their behavior.
Evaluations:
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Surface performance in edge cases before users do.
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Help teams iterate meaningfully, not just instinctively.
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Act as guardrails for responsible deployment.
In short, evaluations are how we “tame” the wild horse — not by diminishing its power, but by honing it. We want agents that remain autonomous and adaptive, but that are also sharper, safer, and more reliable.
The Road Ahead
If you're building with agentic systems, the message is clear: don’t just test — evaluate. Build datasets that simulate the diversity of the real world. Measure more than just accuracy. Look under the hood. And iterate with insight, not just instinct.
The horse is ready. The question is: are you?
