9.8.1 Evaluation and Safety Roadmap: Score, Guard, Trace

An Agent should not only run. You must know whether it succeeded, whether the process was safe, and where the failure happened.

See the Guardrail Stack First

Evaluation tells you whether the system works. Safety tells you what it may do. Observability tells you where it broke.

Run a Launch Scorecard Check

Evaluate both final output and execution process.

run = {
    "task_success": True,
    "tool_error": False,
    "permission_confirmed": True,
    "trace_saved": True,
    "cost_usd": 0.08,
}

launch_ok = (
    run["task_success"]
    and not run["tool_error"]
    and run["permission_confirmed"]
    and run["trace_saved"]
    and run["cost_usd"] < 0.10
)

print("launch_ok:", launch_ok)
print("scorecard:", "task, tools, safety, trace, cost")

Expected output:

launch_ok: True
scorecard: task, tools, safety, trace, cost

One smooth final answer is not enough evidence. Keep replayable tasks and process traces.

Learn in This Order

Step	Read	Practice Output
1	Evaluation methods	Separate result evaluation from process evaluation
2	Benchmarks	Use public benchmarks as reference, not a product replacement
3	Safety and alignment	Identify prompt injection, over-permission, leakage, hallucination
4	Guardrails	Add input filter, output validation, permissions, human confirmation
5	Observability	Save logs, traces, errors, latency, cost, and failure reason

Pass Check

You pass this chapter when every Agent run can be reviewed through goal, plan, tool calls, observations, final answer, safety rule, cost, and failure reason.

The exit mini project is a 10 to 20 task evaluation set plus at least 3 safety rules.