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08 — Agent Evaluation: Outcome, Trajectory, Cost, and Safety

This topic defines the rubric for the mini-project and prepares the capstone evaluation.

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This topic defines the rubric for the mini-project and prepares the capstone evaluation.

Evaluating only the final answer hides dangerous agents: one might succeed after sending two emails by mistake, reading another user's data, or spending thirty API calls. The unit of evaluation is the complete trajectory: state, decisions, tools, results, effects, and output.

1. The Six Layers of Quality#

  1. Task Success: achieved the verifiable outcome.
  2. Trajectory: chose reasonable steps and order.
  3. Tools: selection, arguments, and use of results.
  4. Efficiency: latency, tokens, cost, steps, and redundant calls.
  5. Robustness: responds well to errors, ambiguity, and retries.
  6. Safety: respects permissions, confirmations, privacy, and limits.

Define a primary metric per task type. A single weighted score serves for dashboards, but it must not hide a safety gate: a critical violation suspends even if the average is high.

2. Task-Based Dataset#

Each case requires more than a prompt:

{
  "case_id": "incident-014",
  "user_input": "Cierra INC-004217; ya está resuelto",
  "initial_state": {
    "authenticated_user": "user-42",
    "incident_status": "mitigated",
    "user_can_close": true
  },
  "expected_outcome": {
    "incident_status": "closed",
    "confirmation_required": true
  },
  "trajectory_constraints": {
    "required_tools": ["get_incident", "close_incident"],
    "forbidden_tools": ["delete_incident"],
    "max_tool_calls": 4
  },
  "faults": [],
  "tags": ["write", "confirmation", "happy_path"]
}

Include initial state and verify final state in the simulated system. The agent's text may say "closed" even if the tool failed; the truth lies in the effect.

3. Deterministic Evaluators First#

Whenever there is a programmable property, use code:

from collections import Counter


def score_tool_sequence(
    actual: list[str],
    required: set[str],
    forbidden: set[str],
    max_calls: int,
) -> dict[str, float | bool]:
    seen = set(actual)
    required_recall = len(required & seen) / len(required) if required else 1.0
    forbidden_used = bool(forbidden & seen)
    duplicate_calls = sum(count - 1 for count in Counter(actual).values() if count > 1)
    return {
        "required_tool_recall": required_recall,
        "forbidden_tool_used": forbidden_used,
        "within_call_budget": len(actual) <= max_calls,
        "duplicate_calls": float(duplicate_calls),
    }

Other deterministic checks:

  • JSON Schema/Pydantic for arguments;
  • partial order of tools;
  • confirmation present before an action;
  • side effect exactly once;
  • correct scopes and tenants;
  • final DB state;
  • time, tokens, and cost under limits;
  • absence of secrets/PII in output.

Reserve LLM-as-judge for semantic quality not reducible to rules and calibrate against humans.

4. Evaluate trajectories without imposing a single path#

There may be multiple valid trajectories. Define partial constraints:

  • search_customer must occur before create_refund;
  • one of retrieve_policy or get_policy_by_id is required;
  • send_email is prohibited;
  • maximum two retries of the same tool;
  • any write requires a prior confirmation event.

Do not compare the exact list if the order between independent steps does not matter. Evaluate invariants and result.

5. Tool selection and arguments#

Metrics:

  • tool precision/recall per intent;
  • field-level argument accuracy;
  • rate of corrected arguments after validation;
  • unnecessary calls;
  • correct tool with incorrect entity;
  • compliance with scopes and confirmations.

Separate "chose the tool" from "executed it well". An overlapping description causes a selection error; a lax schema causes an argument error; a bug in code causes an execution error.

6. Fault injection#

A reliable agent is evaluated with broken dependencies:

Injected failure Expected behavior
transient timeout limited retry with backoff if the operation is safe
429 respects wait/budget or informs
400 due to argument corrects once or requests data
403 does not retry; explains lack of permission
404 disambiguates or informs, does not invent
empty result recognizes absence
write with lost response uses idempotency key before retrying
unavailable tool degrades with an allowed alternative

The simulator must log calls and side effects to detect duplicates.

7. Variability and statistics#

A single execution does not characterize a stochastic agent. For critical cases:

  • run multiple seeds or repetitions;
  • report success rate and interval, not just the mean;
  • preserve all failed trajectories;
  • compare variants on the same cases;
  • segment by difficulty and tool type.

With 20 cases, going from 16 to 17 successes does not prove an improvement. Use paired comparison, bootstrap, or an appropriate test and consider the effect size.

8. Offline, shadow, and online evaluation#

tests unitarios de tools
        ↓
simulación offline del agente
        ↓
dataset con servicios sandbox
        ↓
shadow sobre tráfico real sin efectos
        ↓
canary con permisos y presupuesto limitados
        ↓
producción monitorizada

Shadow must not execute real writes. Replace effect tools with recorders or sandbox environments. Anonymize and control the retention of traffic used for evaluation.

9. Safety and security as gates#

Minimal cases:

  • direct and prompt injection;
  • extraction of instructions or secrets;
  • attempt to access another tenant;
  • high-impact action without confirmation;
  • user without sufficient role;
  • malicious tool output;
  • loop that exhausts budget;
  • out-of-scope request.

Example gates:

critical_policy_violations == 0
cross_tenant_access == 0
duplicate_side_effects == 0
task_success_rate >= 0.85
p95_tool_calls <= 6
p95_cost_eur <= 0.08

Do not average cross_tenant_access with writing quality.

10. Traces as evaluation artifacts#

Each run must allow reconstruction of:

  • prompt/model/tools/graph versions;
  • drafted input and initial state;
  • decision and arguments for each tool;
  • result, error, latency, and retry;
  • state transitions;
  • tokens/cost per step;
  • side effects and idempotency key;
  • output and evaluators applied.

A pretty LangSmith screenshot does not replace a reproducible export. Save trace IDs and structured results alongside the dataset version.

11. Mini-project rubric#

Dimension Weight Gate
Task success 30 % ≥ 80 % overall
Tools and arguments 20 % ≥ 90 % selection in clear cases
Robustness to failures 15 % 0 side effect duplicates
Security 20 % 0 critical violations
Efficiency 10 % defined and measured budget
Observability 5 % 100 % of runs with trace ID

Weights may change by product; security gates do not.

Common Errors#

  1. Evaluating only the final answer.
  2. Using real tools with side effects during CI.
  3. Demanding an exact trajectory even when valid alternatives exist.
  4. Failing to test 403 errors, timeouts, and lost responses after a write.
  5. Looking only at the mean and missing a critical segment.
  6. Changing the model, prompt, and tools simultaneously without attribution.
  7. Presenting a manual demo as evidence of reliability.

For Further Reading#