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name: agent-evals-lab description: >- Evaluate agent quality and reliability with practical scorecards: accuracy, relevance, actionability, risk flags, tool-call failures, regression checks, and prioritized fix plans. Use when users ask to audit agent quality, compare prompt/config/model changes, investigate failures, or validate performance after updates.

Agent Evals Lab

Objective

Turn subjective “agent feels better/worse” into measurable quality signals and actionable fixes.

Quickstart (5 minutes)

python3 scripts/eval_score.py \
  --input references/eval-cases.sample.json \
  --risk medium \
  --strict \
  --out /tmp/evals_report.json

Expected output: deterministic scorecard with Go/Conditional Go/No-Go verdict, gate reasons, and by-task deltas.

Use Cases

• Audit current agent quality before production rollout
• Compare baseline vs changed prompt/config/toolchain
• Catch regressions after updates
• Prioritize highest-impact fixes for next sprint

Typical trigger phrases:

• "evaluate this agent" / "audit agent quality"
• "did the last prompt change improve results?"
• "compare model A vs model B"
• "why is this workflow failing?"
• "run regression checks after update"
• "is this ready for production?"

Inputs

Collect or infer:

• Agent purpose and target tasks
• 10-30 representative test cases (prompt + expected outcome)
• Constraints (latency/cost/risk tolerance)
• Environment notes (models/tools/channels)

If test cases are missing, generate a minimal starter set and label as synthetic.

Evaluation Dimensions (required)

Score each case on:

1. Correctness
2. Relevance
3. Actionability
4. Risk flags (safety, compliance, irreversible-action risk)
5. Tool reliability (wrong tool, failed execution, silent fallback)

Use 1-5 scale + short evidence note per dimension.

Execution Workflow

1. Build evaluation set

• Use real cases first, then synthetic gaps.
• Tag each case by task type and risk level.

2. Run baseline evaluation (deterministic)

• Capture outputs + tool behavior.
• Score all required dimensions.
• Run scripts/eval_score.py --input <cases.json> --risk <low|medium|high> --strict.

3. Identify failure clusters

• Factual errors
• Reasoning gaps
• Tool-call failure patterns
• Over/under-asking clarifications
• Hallucinated confidence

4. Propose fixes

• Prompt/process/tool changes
• Rank by expected impact vs effort

5. Re-run focused regression set

• Validate top fixes on high-risk/high-frequency cases

Deterministic Gates

• Hard gate: high-risk workflows cannot be Go if critical minimum score < threshold.
• Hard gate: tool reliability average below threshold => no Go.
• Hard gate: synthetic-only evidence in high-risk mode => no Go.
• Strict mode applies deterministic thresholds before final recommendation.

Required Output Format

1. Executive Summary

• Overall score snapshot
• Top strengths
• Top failure modes

2. Scorecard

• Dimension averages
• By task-type breakdown
• By risk-level breakdown
• Deterministic scorer output snapshot

3. Failure Map

• Cluster name
• Frequency
• User impact
• Root-cause hypothesis

4. Top 5 Fixes (prioritized)

• Change
• Expected impact
• Effort (S/M/L)
• Owner
• Validation test
• Exact implementation command(s) where applicable

5. Regression Plan (1-2 weeks)

• Cases to rerun
• Success thresholds
• Rollback trigger

6. Go/No-Go Recommendation

• Go / Conditional Go / No-Go
• Conditions and next checkpoint date

7. Before/After Delta

• overall delta
• critical delta
• tool reliability delta
• by-task delta

Quality Rules

• Prefer measured evidence over intuition.
• Separate facts, inferences, and recommendations.
• Never claim improvement without before/after evidence.
• For high-risk workflows, require explicit human-in-the-loop checkpoints.
• Include deterministic aggregate evidence before final Go/No-Go when case data is available.

Reference

• Read references/eval-templates.md for reusable case templates and scoring rubrics.
• Read references/ops-report-template.md for the release memo format.