Build Evals Before You Build Agents

Teams often want to jump straight to agents because agents look like leverage. The promise sounds attractive: give the model tools, let it plan, and reduce human workload. But the moment you add tool choice, multi-step reasoning, and side effects, the debugging surface expands dramatically.

Subheader

If you build agent orchestration before evaluation, you lose the ability to tell whether the extra complexity is producing real value or just more impressive failure modes.

TL;DR

Define how success is measured before you wire autonomy. Start with a narrow task, a scoring loop, and visible failure categories. Then decide whether an agent is actually warranted.

Why Agents Hide Weak Product Thinking

Many agent projects begin with architecture instead of task definition. Teams discuss planners, memory, tools, retries, and routing before they can clearly say what a successful outcome looks like.

That sequence is backwards. If the task is unclear, the agent will not fix it. It will simply make the uncertainty more expensive by touching more systems.

The agent layer should be a response to task complexity, not a substitute for product clarity.

What Evals Need to Measure

Agent evals should score more than the final text output. You need visibility into the path as well as the destination.

A useful early evaluation set includes:

• task completion rate
• correctness of chosen tool calls
• rate of unnecessary tool usage
• latency and token cost
• safety boundary adherence
• fallback quality when the task cannot be completed

These metrics create a hard distinction between the agent solved the problem and the agent did many things that felt impressive.

A Better Build Order

Start with a manual or single-step baseline. That gives you a control condition.

Then write a small benchmark set representing the task. It does not need to be large at first. Even twenty carefully chosen examples are more useful than a hundred vague ones.

Then score outputs manually or semi-automatically. Look for repeated failure modes. Only after you understand those patterns should you decide whether extra planning or tool use is actually justified.

In many cases, you discover that a simple retrieval step, one structured tool, or better UI constraints solve the problem more cheaply than a full agent loop.

When an Agent Is Actually Worth It

An agent becomes defensible when the task genuinely requires branching decisions, multiple external systems, or repeated tool choice under uncertainty. Even then, the agent should be kept narrow.

The right question is not can the model act autonomously. The right question is which decisions are safe and valuable to automate, and how will we know when it drifts.

Common Failure Modes

The first failure mode is measuring only final user satisfaction. That is too slow and too fuzzy to guide iteration.

The second failure mode is evaluating only success and not path quality. An agent that succeeds while wasting time, touching the wrong tools, or leaking unsafe behavior is not healthy.

The third failure mode is chasing emergent behavior when simple decomposition would be cheaper. Sometimes the most responsible agent design is not to build one at all.

Key Takeaways

Evals create the discipline that autonomy tries to skip. If you put them first, you learn whether the system deserves more freedom. If you skip them, the project becomes a fog machine.

FAQ

Can I prototype an agent before formal evals exist?

You can prototype quickly, but you should not scale the system or trust its behavior until an evaluation loop exists.

Do evals need to be fully automated?

No. Early evals can include manual review. What matters first is clarity of scoring, not perfect automation.