Evaluation Pipelines & Reliability Benchmarks

Without continuous evaluation, prompting changes will silently break existing functionality. Building reliable agents requires automated eval pipelines running against standardized test datasets.

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Continuous Evals: Run your benchmark suite on every prompt change or model version bump, just like unit tests in continuous integration.

The Three Levels of Agent Evals

1. Step-Level Evals (Tool Selection Accuracy)

Did the agent select the correct tool given the input query?
Are the extracted tool parameters valid and complete?

2. Trajectory Evals (Efficiency & Loop Health)

How many loop iterations did the agent take to reach completion?
Did the agent enter infinite loops or repeat failed tool calls?

3. Outcome Evals (Final Output Quality)

Does the final synthesized output satisfy the user’s intent?
Can an LLM-as-a-judge or deterministic rule verify factual accuracy?

# Minimal trajectory evaluation sample
def evaluate_trajectory(trajectory, expected_tools):
    tools_called = [step["tool"] for step in trajectory if "tool" in step]
    precision = len(set(tools_called) & set(expected_tools)) / len(set(tools_called)) if tools_called else 0
    return {
        "passed": precision >= 0.8,
        "steps_count": len(trajectory),
        "tools_called": tools_called
    }

Summary & Next Steps

Congratulations on finishing the AI Agents Mastery track! You now understand the full lifecycle of designing, architecting, scoping, and evaluating autonomous agentic software.