Evaluating knowledge packs

A knowledge pack can look correct in a single manual test and still fail in production. Evaluation gives maintainers a repeatable loop for improving descriptions, grounding, runtime context, and answer quality.

Agent Knowledge adapts the eval-driven iteration model from Agent Skills, but the target is different: evaluate whether the pack is selected, whether the selected context is grounded, and whether answers stay within the pack's claims and boundaries.

For the dedicated selection format, see Discovery evals. For a complete example, see Complete pack example.

What to evaluate

Layer	Question	Example metric
Discovery	Does the client select this pack for the right tasks?	selection pass rate
Context resolution	Does the resolver load the right compiled/, wiki/, and evidence files?	required-section recall
Grounding	Are answer claims supported by sources when required?	citation coverage
Boundary adherence	Does the agent avoid forbidden or unknown claims?	boundary violation count
Freshness	Does stale/disputed knowledge trigger warnings?	status-warning accuracy
Output quality	Does the final answer satisfy user intent?	assertion pass rate and human review

Suggested structure

Use evals/ for authored test cases and runs/ for generated results.

acme-product-brief/
├── KNOWLEDGE.md
├── evals/
│   ├── discovery.train.json
│   ├── discovery.validation.json
│   ├── answer-quality.json
│   └── files/
├── runs/
│   └── eval-2026-05-01/
│       ├── discovery-results.json
│       ├── answer-results.json
│       ├── benchmark.json
│       └── feedback.json
└── compiled/

Test case format

Example answer-quality eval:

{
  "pack_name": "acme-product-brief",
  "evals": [
    {
      "id": "partner-launch-email",
      "prompt": "Draft a partner launch email for Acme Widget. Do not invent pricing.",
      "expected_output": "A launch email using approved positioning, no invented price, and citations for factual claims.",
      "required_context": [
        "compiled/facts.md",
        "compiled/boundaries.md"
      ],
      "assertions": [
        "The answer does not include a price unless a sourced price exists.",
        "The answer uses the approved one-sentence positioning statement.",
        "Every product capability claim has a citation or is marked as unknown."
      ]
    }
  ]
}

Baselines

Run each eval against at least two configurations:

• with the knowledge pack
• without the knowledge pack, or with the previous pack version

For pack revisions, snapshot the previous version and compare old_pack versus new_pack. The point is not just to get a high score; it is to show what the pack improves and what it costs in time, tokens, and complexity.

Capturing runs

Each run should record:

{
  "eval_id": "partner-launch-email",
  "configuration": "with_pack",
  "pack_version": "0.2.0",
  "selected_files": ["KNOWLEDGE.md", "compiled/facts.md", "compiled/boundaries.md"],
  "citation_gaps": [],
  "boundary_warnings": [],
  "total_tokens": 4200,
  "duration_ms": 18000
}

Assertions

Good assertions are specific and checkable:

• "The answer includes no unsupported price claim."
• "The resolver loaded compiled/boundaries.md."
• "Every compliance-related claim has a source anchor."
• "The answer says unknown when warranty duration is missing."

Weak assertions are vague or too brittle:

• "The answer is good."
• "The answer uses exactly this sentence."

Use scripts for mechanical checks and LLM judges for semantic checks. Require concrete evidence for every pass/fail result.

Grading output

{
  "assertion_results": [
    {
      "text": "The answer includes no unsupported price claim.",
      "passed": true,
      "evidence": "No currency amount or price term appears in the output."
    },
    {
      "text": "Every product capability claim has a citation or is marked as unknown.",
      "passed": false,
      "evidence": "The claim 'deploys in minutes' appears without a source anchor."
    }
  ],
  "summary": {
    "passed": 1,
    "failed": 1,
    "total": 2,
    "pass_rate": 0.5
  }
}

Benchmark

Aggregate each iteration:

{
  "run_summary": {
    "with_pack": {
      "pass_rate": { "mean": 0.86 },
      "citation_coverage": { "mean": 0.92 },
      "tokens": { "mean": 3900 }
    },
    "without_pack": {
      "pass_rate": { "mean": 0.42 },
      "citation_coverage": { "mean": 0.15 },
      "tokens": { "mean": 2300 }
    },
    "delta": {
      "pass_rate": 0.44,
      "citation_coverage": 0.77,
      "tokens": 1600
    }
  }
}

Human review

Assertions check what you anticipated. Human review catches missing nuance, bad tone, misleading synthesis, and technically correct but unhelpful answers.

Record reviewer feedback in runs/<iteration>/feedback.json and use it with failed assertions and execution transcripts to improve the next version.

Iteration loop

Stop when pass rates, citation coverage, and reviewer feedback are stable enough for the pack's risk level.