You can't improve what you can't see

With a plain prompt you judge one output. An agent produces a trajectory — a whole sequence of reasoning, tool calls, and results — and any step can be where it went wrong. So the first requirement for reliable agents is observability: capturing that full trajectory so you can see what actually happened.

Tracing

A trace records every step of a run: each model decision, the tool called, the arguments, the result, tokens spent, and time taken. Tools like LangSmith and Helicone visualize traces so you can replay a run and pinpoint the exact step that derailed. Without tracing, debugging an agent is guesswork; with it, the failing step is usually obvious.

Evaluating agents

Beyond watching individual runs, you need to know if the agent is good — and stays good as you change it. Two complementary lenses:

• Final-outcome eval — did it achieve the goal? Build a set of tasks with known correct results and score pass/fail.
• Trajectory eval — how did it get there? Did it use the right tools, avoid needless steps, stay on goal? A correct answer reached by a lucky, wasteful path is fragile.

An LLM can act as a judge to score outcomes at scale, but anchor it with a handful of hand-checked tasks so you trust the judge.

Make it a loop

An agent without traces is a black box you can only pray at. Capture every step, build a small task set you can re-run, and every change becomes a measured improvement instead of a hopeful guess.

Try this: Add simple logging to one agent — print each step's decision, tool, and result. Run a task and read the log end to end. Even plain print statements turn an agent from an inscrutable oracle into something you can actually debug and improve.