Agent Tune Kit

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Agent Tune Kit is a local Codex plugin for moving your existing local Agent from "it runs" to "it can be evaluated, diagnosed, and iteratively tuned."

It focuses on two jobs: first turn evaluation data into a reusable, human-reviewable asset; then connect Agent batch evaluation, failure discovery, reporting, failure review, and Codex-driven tuning into a repeatable loop.

Architecture

Who It Is For

For developers with an existing local Agent who want to evaluate and tune it. To start, prepare:

• A local Agent, chatbot, tool-using Agent, or RAG Agent.
• A small evaluation dataset, preferably CSV; 5 to 20 rows are enough to start.
• Inputs, expected answers, or human-checkable results.
• A desire to let Codex help locate weak spots and tune prompts, code, parameters, or tool configuration.

Project Value

Agent Tune Kit is not just a way to run one test. It separates Agent tuning into two clear paths:

• Dataset Preparation: generate a dataset from business context, examples, or rules; enrich ground_truth; review the dataset in local HTML; and correct expected results from human feedback. The dataset is stored under .atk/datasets/ and is not tied to a single evaluation run.
• Agent Evaluation and Tuning: connect an existing Agent to a runner, run batch evaluation, find failure cases, generate an analysis report, review failures in local HTML, and let Codex tune the Agent from evidence. Each run writes to .atk/results/vN/ so you can validate whether later versions actually improve.

This turns Agent tuning from one-off subjective trial and error into an engineering workflow with samples, results, reports, and tuning records.

Install

One-command install:

uvx --from agent-tune-kit atk install

To keep the atk command available:

uv tool install agent-tune-kit
atk install

Or use pipx:

pipx install agent-tune-kit
atk install

To install from local source, update your checkout to the latest code on your chosen branch, then run from the ATK repository root:

uv tool install --reinstall .
atk install

. builds and installs the ATK package from the current directory; --reinstall includes local changes even when the version is unchanged. Rerun both commands after updating the code. atk install clears all ATK cache versions in the personal marketplace after installation validation succeeds, including same-version reinstalls. Failed installation preserves the cache; failed cache cleanup returns an error.

After installation, refresh the plugin list in Codex:

/plugins

Select and enable Agent Tune Kit, then restart Codex and open a new session. Existing sessions do not automatically replace instructions already read; finish active ATK tasks before reinstalling.

Run atk status to compare installed and cached plugin contents. It reports pending Codex loading when no cache exists. Incomplete installation, stale/unreadable caches and version mismatches return exit code 1; matching or pending caches return 0. This does not confirm plugin enablement or activation in a session. See the installation and cache notes.

Two Core Paths

Run these commands in your Agent project, not in this repository.

Ideally, you already have a local Agent project that Codex can inspect and edit, plus an evaluation dataset. CSV is recommended, but column names do not need to follow a strict schema; Codex will infer inputs, expected results, and evaluation shape from the data.

Path A: Dataset preparation (as needed)

Use one entry point to prepare or revise data; reuse suitable existing data directly:

$atk-dataset Prepare data/eval.csv for evaluation. Preserve existing fields and answers; fill only grounded gaps in the judgment criteria.

You can also supply business examples or directly request a correction for a specific atk_id. Sample construction, grounded criteria and feedback updates happen within the same Skill as needed.

• The default artifact remains .atk/datasets/dataset.csv, preserving existing columns, valid IDs, row order and unrelated values.
• Reuse expected, label, acceptance_criteria or ground_truth; no mandatory canonical answer column or global answer style.
• Missing trustworthy criteria remain gaps and produce unknown judgments downstream, not invented answers.
• Call $atk-visualize-dataset only when human browsing helps. Existing HTML and dataset_review.csv exports remain supported; return feedback to $atk-dataset. Direct feedback needs no export.

Preparation does not run the Agent or create result versions. Continue to $atk-init when not connected, or $atk-eval with a compatible runner. Dataset/criteria corrections never rewrite historical results or prove Agent improvement.

Path B: Agent Evaluation and Tuning

Use this loop when you already have a runnable Agent and an evaluation dataset:

$atk-init My Agent entrypoint is scripts/agent.py and the evaluation dataset is data/eval.csv
$atk-eval
$atk-diagnose
$atk-report
$atk-visualize-failures
$atk-optimize

$atk-diagnose combines deterministic checks and Codex semantic judgment. It prepares or reuses rules internally when needed; no mode selection is required. It keeps confirmed failures in failure_cases.csv and records every executed case in judgments.csv (passed/failed/unknown, with reasons). Unknowns never count as passes or enter --only-failures. Legacy results still show known failures, but missing judgments cannot prove success. Continue with $atk-report for detailed analysis and cross-version review, then $atk-optimize.

Verify Improvement

After tuning, run another loop. The common path is to rerun only the prior failures:

$atk-eval --only-failures
$atk-diagnose
$atk-report

New results are written to a new .atk/results/vN/. --only-failures maps the prior failure_cases.csv back to .atk/datasets/dataset.csv by atk_id and reruns only those rows. Starting with the second loop, $atk-report compares against the previous tuning_plan.md and tells you whether the target issues were resolved, partially resolved, unresolved, or impossible to judge.

Local Tuning Context

ATK can optionally read local private .atk/context.md to preserve user-confirmed tuning objectives, Agent behavior standards, ground_truth judgment standards, human feedback, and tuning decisions. It is not a dataset metadata or run-log file: field names, row counts, result paths, execution metrics, and other facts recoverable from .atk/datasets/, .atk/results/, or runner files should not be recorded there.

$atk-dataset updates reusable standards when confirmed by the user; $atk-diagnose, $atk-report and $atk-optimize use them. Missing .atk/context.md does not block the workflow.

Output Structure

.atk/
├── datasets/
│   └── dataset.csv        # ATK runnable dataset with atk_id
├── runner/
│   ├── eval_runner.py
│   └── failure_rule.py
└── results/
    ├── v1/
    │   ├── eval_results.csv
    │   ├── judgments.csv
    │   ├── failure_cases.csv
    │   ├── failure_cases.html
    │   ├── report.md
    │   └── tuning_plan.md
    └── v2/
        └── ...

Common output files:

• eval_results.csv: actual Agent output for each row.
• failure_cases.csv: rows selected as failures.
• failure_cases.html: optional failure review page.
• report.md: analysis and tuning recommendations.
• tuning_plan.md: what Codex changed and why.

Common Skills

• $atk-dataset: prepare or revise evaluation data while preserving fields and IDs, with feedback applied as needed.
• $atk-visualize-dataset: render .atk/datasets/dataset.csv into a local HTML browser for quickly reviewing rows and expected-result fields.
• $atk-init: generate the test runner.
• $atk-eval: run evaluation and create a new result version.
• $atk-diagnose: identify failures and unknowns using rules and semantic judgment, with initial cause investigation.
• $atk-report: generate analysis and cross-loop validation.
• $atk-visualize-failures: generate the failure review HTML page.
• $atk-optimize: tune the Agent based on the report.