agentv-bench

AgentV Bench

A skill for evaluating agents and iteratively improving them through data-driven optimization.

At a high level, the process goes like this:

• Understand what the agent does and what "good" looks like
• Write evaluation test cases (EVAL.yaml or evals.json)
• Run the agent on those test cases, grade the outputs
• Analyze the results — what's working, what's failing, and why
• Improve the agent's prompts/skills/config based on the analysis
• Repeat until you're satisfied

Your job when using this skill is to figure out where the user is in this process and then jump in and help them progress. Maybe they want to start from scratch — help them write evals, run them, and iterate. Maybe they already have results — jump straight to analysis and improvement.

Be flexible. If the user says "I don't need a full benchmark, just help me debug this failure", do that instead.

After the agent is working well, you can also run description optimization to improve skill triggering accuracy (see references/description-optimization.md).

Communicating with the user

This skill is used by people across a wide range of familiarity with evaluation tooling. Pay attention to context cues:

• "evaluation" and "benchmark" are borderline but OK in most cases
• For "YAML", "evaluator", "assertion", "deterministic judge" — see serious cues from the user that they know what those mean before using them without explanation
• Briefly explain terms if in doubt

When presenting results, default to summary tables. Offer detail on request. In CI/headless mode, skip interactive prompts and exit with status codes.

---

Step 1: Understand the Agent

Before running or optimizing, understand what you're working with.

1. Read the agent's artifacts — prompts, skills, configs, recent changes. Understand the full picture: what tools are available, what the expected input/output looks like, what constraints exist.

2. Identify success criteria — what does "good" look like for this agent? What are the edge cases? What would a failure look like? Talk to the user if this isn't clear from the artifacts alone.

3. Understand the target harness — which provider runs the agent (Claude, GPT, Copilot CLI, Gemini, custom CLI)? This affects what evaluator types are available and how to run tests. Targets are configured in .agentv/targets.yaml (canonical location, searched from the eval file directory upward). Sensitive values like api_key must use ${{ ENV_VAR }} syntax — literal secrets are rejected as a security guardrail.

4. Challenge assumptions — if evals already exist, review their quality before running:

   • Are the test cases testing the right things?
   • Are assertions specific enough to catch real failures?
   • Are there ambiguous or contradictory test cases?
   • Flag eval issues before proceeding — running bad evals wastes time.

5. Check integrity — ensure task prompts (what the agent receives) are not also used as evaluator prompts (how outputs are scored). If a prompt file appears in both locations, note the overlap and optimize only for the task purpose.

---

Step 2: Write Evaluations

AgentV supports two evaluation formats:

EVAL.yaml (native, full features) — supports workspaces, code graders, multi-turn conversations, tool trajectory scoring, workspace file tracking, multi-provider targets. Use this for agent evaluation.

# example.eval.yaml
tests:
  - id: basic-code-review
    input: "Review this TypeScript file for bugs and suggest improvements"
    criteria: "Identifies the null pointer bug on line 12 and suggests a fix"
    assertions:
      - type: contains
        value: "null"
      - type: llm-grader
        prompt: "Did the review identify the bug and suggest a concrete fix?"

workspace:
  template: ./workspace-template
  hooks:
    before_each:
      reset: fast

Multi-skill evaluation is handled naturally via input messages — describe the task in the test input, and the agent uses whatever skills it needs.

evals.json (skill-creator compatible) — auto-promoted to EVAL-equivalent format:

• prompt → input messages
• expected_output → reference answer
• assertions → evaluators
• files[] paths resolved relative to the evals.json location

{
  "skill_name": "my-agent",
  "evals": [
    {
      "id": 1,
      "prompt": "User's task prompt",
      "expected_output": "Description of expected result",
      "assertions": ["Output includes error handling", "Uses async/await"]
    }
  ]
}

Writing good test cases

Start with 2-3 realistic test cases — the kind of thing a real user would actually say. Share them with the user before running: "Here are a few test cases I'd like to try. Do these look right, or do you want to add more?"

Good assertions are objectively verifiable and have descriptive names. Subjective quality ("the output is good") is better evaluated qualitatively — don't force assertions onto things that need human judgment.

Evaluator types (cheapest to most expensive): exact, contains, regex, is-json, field-accuracy, composite, code-grader, tool-trajectory, llm-grader. See references/eval-yaml-spec.md for full config and grading recipes for each type.

Prefer deterministic evaluators over LLM graders whenever possible. If an assertion can be checked with contains or regex, don't use llm-grader.

---

Step 3: Run and Grade

This section is one continuous sequence — don't stop partway through.

Each run produces a new .agentv/results/runs/<timestamp>/ directory automatically. Use timestamps to identify iterations when comparing runs.

Choosing a run mode

User instruction takes priority. If the user says "run in subagent mode", "use subagent mode", or "use CLI mode", use that mode directly.

If the user has not specified a mode, default to subagent.

SUBAGENT_EVAL_MODE	Mode	How
subagent (default)	Subagent mode	Subagent-driven eval — parses eval.yaml, spawns executor + grader subagents. Zero CLI dependency.
cli	AgentV CLI	agentv eval <path> — end-to-end, multi-provider

Set SUBAGENT_EVAL_MODE in .env at the project root as the default when no mode is specified. If absent, default to subagent. User instruction always overrides this.

subagent — Parses eval.yaml directly, spawns executor subagents to run each test case in the current workspace, then spawns grader subagents to evaluate all assertion types natively. No CLI or external API calls required. Read references/subagent-pipeline.md for the detailed procedure.

cli — AgentV CLI handles execution, grading, and artifact generation end-to-end. Works with all providers. Use when you need multi-provider benchmarking or CLI-specific features.

Running evaluations

AgentV CLI mode (end-to-end, EVAL.yaml):

agentv eval <eval-path> --artifacts .agentv/artifacts/

Subagent mode — read references/subagent-pipeline.md for the detailed procedure. In brief: use pipeline input to extract inputs, dispatch one executor subagent per test case (all in parallel), then proceed to grading below.

Spawn all runs in the same turn. For each test case that needs both a "with change" and a "baseline" run, launch them simultaneously. Don't run one set first and come back for the other — launch everything at once so results arrive around the same time.

Multi-target benchmarking:

agentv eval <eval-path> --target claude --target gpt --target copilot

Baseline strategy:

• New agent: baseline is "no prompt" or minimal prompt — same eval, no agent-specific configuration
• Improving existing: snapshot the current version before editing (cp -r <prompt-dir> <workspace>/prompt-snapshot/), use as baseline throughout
• Multi-target: each target is its own baseline — no need for a separate "without" run

While runs are in progress, draft evaluators

Don't just wait for runs to finish — use this time productively. If assertions don't exist yet, draft them now. If they exist, review them and explain what they check to the user.

Good assertions are discriminating — they pass when the agent genuinely succeeds and fail when it doesn't. An assertion that passes for both good and bad outputs is worse than no assertion.

As runs complete, capture timing data

When each subagent task completes, you receive a notification containing total_tokens and duration_ms. Save this data immediately to timing.json in the run directory. See references/schemas.md for the timing.json schema.

This is the only opportunity to capture this data — it comes through the task notification and isn't persisted elsewhere. Process each notification as it arrives.

Grading

In CLI mode, agentv eval handles all grading end-to-end — no manual phases needed.

In subagent mode, grading has three phases. All three are required — do not stop after phase 1.

Phase 1: Code graders (deterministic, zero-cost)

agentv pipeline grade <run-dir>

This runs all code-grader assertions against the response.md files. Results are written to <test-id>/code_grader_results/<name>.json. Alternatively, pass --grader-type code to pipeline run to run code graders inline.

Phase 2: LLM grading (semantic — do NOT skip this phase)

Dispatch one grader subagent per (test × LLM grader) pair, all in parallel. Do not write a script to call an LLM API instead — the grader subagents use their own reasoning, which IS the LLM grading. Example: 5 tests × 2 LLM graders = 10 grader subagents launched simultaneously.

Do NOT dispatch a single grader for multiple tests. Each subagent grades exactly one (test, grader) pair.

Each grader subagent (read agents/grader.md):

1. Reads <test-id>/llm_graders/<name>.json for the grading prompt
2. Reads <test-id>/response.md for the candidate output
3. Grades the response against the prompt criteria
4. Writes its result to disk: <run-dir>/<evalset>/<test-id>/llm_grader_results/<name>.json
5. Returns score (0.0–1.0) and per-assertion evidence to the orchestrator

Writing to disk is critical. Assertion arrays are lost if accumulated only in the orchestrator's context across multiple batches (context summarization drops detail). Writing per-test results to llm_grader_results/<name>.json makes grading resumable and assertion evidence durable.

The result file format is:

{ "score": 0.85, "assertions": [{"text": "...", "passed": true, "evidence": "..."}] }

After all grader subagents complete, run Phase 3 directly.

Phase 3: Merge and validate

agentv pipeline bench <run-dir>
agentv results validate <run-dir>

pipeline bench reads LLM grader results from llm_grader_results/<name>.json per test automatically, merges with code-grader scores, computes weighted pass_rate, and writes grading.json + index.jsonl + benchmark.json.

Artifacts

All artifacts use established schemas — see references/schemas.md for the full definitions. Do not modify the structure. Key artifacts per run:

• grading.json: per-test assertions with {text, passed, evidence}, plus summary
• timing.json: {total_tokens, duration_ms, total_duration_seconds}
• benchmark.json: per-target aggregate {pass_rate, time_seconds, tokens}

Write artifacts to .agentv/artifacts/ or the iteration directory.

Workspace features (EVAL.yaml only)

• Workspace isolation — clone repos, run setup/teardown hooks (before_all, before_each, after_each, after_all)
• Materialization modes — pooled (reuse slots), temp (fresh per run), static (existing dir)
• Multi-repo — clone multiple repos with sparse checkout and shallow clone support
• File change tracking — grade by diffing workspace files before/after agent execution

---

Step 4: Analyze Results

Once all runs are graded, analyze the results before attempting improvements.

Pattern analysis

Read the JSONL results and look for:

• Always-pass tests — assertion too loose or non-discriminating. If it passes for both good and bad outputs, it's not testing anything.
• Always-fail tests — task impossible, eval broken, or assertion misconfigured. Don't optimize against broken evals.
• Flaky tests — non-deterministic results across runs. Investigate before treating failures as real.
• Systematic failures — same failure pattern across multiple tests. This usually points to a missing instruction or wrong approach.
• Deterministic upgrade candidates — llm-grader assertions that could be replaced with contains, regex, or is-json (cheaper, faster, more reliable).

Dispatch subagents

• Dispatch analyzer (read agents/analyzer.md) for a structured quality audit: deterministic upgrade suggestions, weak assertion detection, cost/quality flags, and benchmark pattern analysis.

• Dispatch comparator (read agents/comparator.md) for blind N-way comparison between iterations or targets. The comparator blinds provider identities, generates task-specific rubrics, scores each output, then unblinds and attributes improvements.

Trace analysis

Use CLI tools for deeper investigation:

agentv trace <results-file>          # Detailed execution trace inspection
agentv compare <file-a> <file-b>     # Structured diff between runs

Look for: tool call patterns, error recovery behavior, conversation flow, wasted steps.

Present results to the user

Show a summary table:

| Test ID          | Score | Pass/Fail | Delta | Notes                    |
|------------------|-------|-----------|-------|--------------------------|
| basic-code-review| 0.85  | ✓ PASS    | +0.15 | Found the bug this time  |
| edge-case-empty  | 0.00  | ✗ FAIL    | —     | Crashed on empty input   |

Highlight:

• Current pass rate and delta from baseline
• Comparison results (which target/iteration won and why)
• Analyst observations the aggregate stats would hide

Ask: "How does this look? Anything you'd change about the evals or the approach?"

---

Step 5: Improve

This is the heart of the loop. You've run the test cases, analyzed the results, and now you need to make the agent better.

How to think about improvements

1. Generalize from the analysis. You're iterating on a small eval set, but the agent will be used on many different inputs. Don't overfit to specific test cases. Rather than fiddly patches or oppressively rigid MUSTs, try different approaches and see what works. It's cheap to experiment.

2. Keep the prompt lean. Read the execution transcripts, not just the final outputs. If the agent wastes time on unproductive steps, remove the instructions causing that. If it always ignores a section, that section isn't pulling its weight.

3. Explain the why. Today's LLMs are smart. They have good theory of mind and can go beyond rote instructions when given good reasoning. If you find yourself writing ALWAYS or NEVER in all caps, that's a yellow flag — reframe as an explanation of why the thing matters. That's more humane, powerful, and effective.

4. Look for repeated work. Read the transcripts from test runs and notice if the agent independently takes the same multi-step approach to something across cases. If all test runs result in writing the same helper script, bundle it. If every run makes the same mistake, the instruction is missing or unclear.

Applying changes

• Surgical edits: ADD (new rule for a missing constraint), UPDATE (refine for clarity), DELETE (remove redundant or harmful rules), NEGATIVE CONSTRAINT (explicitly state what NOT to do)
• One change per iteration to isolate effects. If you change three things and the score improves, you don't know which change helped.
• Variant tracking: When a change helps some tests but hurts others, maintain 2-3 prompt variants. Compare variants to find the best overall approach before converging.
• When converging: Generalize specific patches into broad principles. Remove redundancy and contradictions. Ensure the prompt is clear, focused, and under 200 lines.

Evaluation integrity

Critical: Only optimize task prompts (what the agent receives), never judge prompts (how evaluators score outputs). Modifying judge prompts games the evaluation without improving the agent.

If a prompt file is referenced in both task input and evaluator configs, optimize for the task purpose only. Document which prompts were modified in the optimization log.

The iteration loop

After improving:

1. Apply your changes to the agent's prompts/skills/config
2. Re-run all test cases (agentv creates a new .agentv/results/runs/<timestamp>/ directory automatically)
3. Compare against the previous iteration (Step 4)
4. Present results to the user
5. Stop when ANY of:
   • The user says they're happy
   • Feedback is all empty (everything looks good)
   • You're not making meaningful progress (no improvement for 2 consecutive iterations)
   • Target pass rate is reached
   • Maximum iterations exhausted

Human checkpoints: At iterations 3, 6, and 9, always present progress to the user regardless of automation settings. Push back if optimization is accumulating contradictory rules or overfitting to specific test cases.

---

Entering Mid-Lifecycle

Users can start at any step by providing existing data:

Entry point	Required input	Example prompt
Step 1 (Understand)	eval-path	"Optimize my agent against evals/support.yaml"
Step 2 (Write Evals)	Agent artifacts	"Write evals for this agent"
Step 3 (Run + Grade)	eval-path	"Run this eval and show me results"
Step 4 (Analyze)	results-path	"Analyze why my agent is failing on these results"
Step 5 (Improve)	Analysis + strategy	"Apply these optimization suggestions"

When entering mid-lifecycle, run only the requested step and subsequent steps. Don't re-run earlier steps unless the user requests a full loop.

---

Advanced: Blind Comparison

For situations where you want a rigorous comparison between two versions (e.g., "is the new version actually better?"), dispatch the comparator subagent. It blinds identities, generates task-specific rubrics, scores outputs, then unblinds and explains why the winner won.

This is optional and requires subagents. The human review loop is usually sufficient.

---

Description Optimization

After the agent is working well, offer to optimize the skill's description field for better triggering accuracy. Read references/description-optimization.md for the full procedure (generate trigger EVAL.yaml, review with user, iterate, apply).

---

Environment Adaptation

For provider-specific notes (Copilot, Codex, Claude SDK, custom CLI), CI/headless mode behavior, and fallback strategies when subagents aren't available, read references/environment-adaptation.md.

---

Subagent Reference

The agents/ directory contains instructions for specialized subagents. Read them when you need to spawn the relevant subagent.

Agent	File	Purpose	When to dispatch
executor	agents/executor.md	Perform test case tasks as the target agent	Step 3 (agent targets — one per test case)
grader	agents/grader.md	Grade responses with per-assertion evidence	Step 3 (grading — one per test × LLM grader pair)
comparator	agents/comparator.md	Blind N-way comparison + post-hoc analysis	Step 4 (comparing iterations/targets)
analyzer	agents/analyzer.md	Quality audit, deterministic upgrades, benchmarks	Step 4 (pattern analysis)

The references/ directory has additional documentation:

• references/eval-yaml-spec.md — Eval YAML schema and assertion grading recipes
• references/subagent-pipeline.md — Detailed subagent-mode pipeline commands and output structure
• references/description-optimization.md — Skill description optimization workflow
• references/environment-adaptation.md — Provider-specific notes and CI/headless behavior
• references/schemas.md — JSON schemas for all artifacts (grading.json, benchmark.json, etc.)
• references/migrating-from-skill-creator.md — Guide for users coming from Anthropic's skill-creator

---

Repeating the core loop for emphasis:

• Understand what the agent does
• Write evaluation test cases
• Run the agent and grade outputs
• Analyze results — surface patterns, dispatch analyst and comparator subagents
• Improve the agent based on analysis
• Repeat until you and the user are satisfied

Take your time with improvements. Read the transcripts. Understand why failures happened. Make changes that generalize beyond the test set. This is important work.