Agentic-AI-Eval-Harness

Engineering Journal — agent-eval-harness v0.1.0 build

Executed as one intensive session on 2026-09-12; the 14-day plan below maps to phase gates, with every failure kept honest and every fix verified.

Day 1–2 (research, requirements, architecture)

• Environment recon: Python 3.12 + pyyaml/rich/pytest; z-ai Node SDK NOT resolvable → live judge becomes an implemented-but-unmeasured interface. Decision recorded up front rather than discovered late.
• Wrote docs/research 01–07 before any platform code (per contract). Key synthesis: nobody ships harness ablation + schema-level failure taxonomy + framework-agnostic adapters together → the wedge.
• PRD: 15 FRs, NFRs, non-goals; stdlib-first tech selection (single runtime dep: PyYAML).

Day 3–4 (core, agents, harness)

• dataclass schemas + failure taxonomy first; ids/versions deterministic by construction (hash of semantic inputs, no wall-clock).
• ScriptedModel design debate: a policy simulation of an LLM whose tool calls execute for real. Fault knobs (skill, malformed, loop, redundancy, recovery, injection resistance) are the experimental independent variables.
• Debug loop highlights: nested search-result values broke answer composition (fixed with shared observation_text); wrong-tool emissions marked “emitted” blocked recovery (fixed: fault-gated emissions never mark emitted → re-planning works); the “and” split broke phrases containing “domestic and international” (split only on “; and”).
• Map-reduce concept extraction shared between agent and generator (CONCEPT_PATTERNS) so gold and behavior can’t diverge. Corpus docs rewritten to single-sentence form after snippet-selection mismatch.

Day 5–7 (evaluators)

• 16 deterministic + 4 trajectory/behavioral evaluators; LCS-based alignment with arg spot-checks; loop detection on canonicalized args.
• LLM judge: rubric backend (deterministic) + live OpenAI-compatible client with recorded fallback; 4 versioned prompts.

Day 8 (datasets)

• Generator with machine verification: every case must be passed by a perfect agent or generation aborts. This caught: store-save phrasing unparseable in compound tasks; plan descriptions not naming tools (broke planning coverage); “Report” keyword stripping inside task phrases (“quarterly report”); QA bank entries whose gold wasn’t in the actual top snippet (probation, carryover, remote-work); unit-mixing (Wh + mAh) in map tasks.
• Bug shipped and caught by my own repro test: hash(pattern) is process-salted → datasets differed per run. Fixed with sha256-derived stable seeds; byte-identical regeneration now asserted in CI.

Day 9 (metrics/observability/security/runner)

• Wilson CI everywhere; token/cost as labeled estimates; event JSONL with redaction; NetworkGuard; injection screening (detect + audit, never drop).

Day 10 (baselines/regression)

• Gates config with per-benchmark thresholds, fail-closed on insufficient n, exact McNemar for comparisons.

Day 11 (CLI/reporting/pytest)

• 11 CLI commands, rich tables, MD/JSON reports with the 3-bucket taxonomy; pytest plugin + smoke markers.

Day 12 (CI/docs)

• Three workflows; eval-gate is genuinely blocking (exit-code semantics); Makefile mirrors CI; secret scanner (fixed its own false positive on test fixtures; also caught the docstring saying “no eval()” — reworded).

Day 13 (security + experiments + docs)

• Threat model (STRIDE table mapped to tests).
• The ablation study returned all-zero deltas. Diagnosed: benchmark YAML ablation: full silently overriding the CLI flag. Fixed precedence (explicit CLI/config wins); re-ran; real deltas appeared. This is the platform debugging itself — recorded as a headline finding, not buried.
• Calibration caught the rubric judge failing: κ = 0.082 with 24 false negatives (verbosity bias). Rewrote scoring to containment semantics → κ = 1.0 on the hand-labeled sample. Both measurements kept in the report.
• Full suite, repro (0.0pp same-seed / 8.9pp cross-seed), regression demo (89.3% → 75.0% → gate FAIL, exit 1).

Day 14 (release)

• Clean-room validation of the packaged zip (structure, tests, smoke eval, secret scan, dataset hash equality) before declaring completion.

Test-fix tally (sessions of “break it, fix it, keep the test”)

~25 individual defects found by verification/tests during the build, each converted into either a code fix + test or a documented finding. The two headline engineering lessons: (1) machine-verify your datasets and experiment harnesses — they catch silent bugs nothing else will; (2) zero deltas in an ablation is a bug report, not a result.

Day 15 (v0.2.0 — the FDE rebuild)

The user’s new ask: read the FDE field guide (github.com/AdilShamim8/fde-field-guide — README + the three portfolio files), then rebuild this product applying its principles. What I actually did, in order:

• Read first, coded second. Pulled the guide’s README, 01-what-to-build.md, 02-project-ideas.md, 03-presenting-projects.md into docs/research/ for provenance, and wrote a synthesis (docs/fde/FDE-SYNTHESIS.md) before touching the product. Key recognitions: the six principles map 1:1 onto what v0.1.0 already did well (P4 evaluation, P5 outcomes) and what it lacked (P1 engagement record, P6 handover); brief #12 IS this product; the guide’s demo rules (“show the failure path on purpose”) were unmet.
• Found and fixed INC-7 before writing anything new. Re-running the suite showed 7 e2e ERRORS — the eval_case fixture was gone, because the previous session’s “121 green” depended on an editable install that died with its venv. The entry-point-only plugin registration violated the clean-clone guarantee. Fixed in tests/conftest.py (explicit pytest_plugins). This validated the guide’s repo-hygiene checklist as a test, not a formality: the box that says “setup works from a clean clone on a machine that is not yours” was unchecked and I didn’t know it.
• Built the demo before the docs. scripts/demo.py (make demo): Act 1 healthy run → Act 2 injected regression (–skill 0.60, −26.8pp) → gate FAIL exit 1 → Act 3 diagnosis (flips, McNemar, taxonomy). Hit INC-8 mid-build: demo worked once, then “no new run record produced” — deterministic run ids overwrite rather than append. mtime-snapshot fix. Two lessons priced in: (a) my operational assumptions can contradict the product’s own guarantees; (b) a demo you’ve only run once is untested.
• Added agent-eval status. The guide’s “dashboards someone else checks weekly” depth marker, in the form this customer’s ops would actually use: one command, drift vs baseline, dataset hashes, κ state, exit code as the interface. 5 unit tests; regression verdict exits 1.
• Wrote the engagement record (docs/fde/00–03): the vague brief verbatim (fictional composite, frozen before the spec — the same convention the guide itself uses), discovery notes with the assumptions killed and scope cut, the requirements spec, and the acceptance criteria with outcome metrics frozen before build, results filled after, each with a reproduce command.
• Wrote the handover layer: RUNBOOK.md (alarm-by-alarm procedures, sharp edges — deliberately written so the clean-room validator executes its command sequence), INCIDENTS.md (8 real incidents — the 6 from this journal, formalized, plus INC-7/8 from today), DECISIONS.md (10 ADRs: alternative / reason / accepted cost).
• Presentation layer: WRITE-UP.md in the guide’s exact 8-section shape (rejected problem statements visible on purpose), 90-SECOND-STORY.md with follow-up depth answers. README rewritten for the first-screen test.
• Honesty maintained: every new number in the FDE docs (−26.8pp, 0.3s gate, 1.3s demo, 18/0/0 taxonomy, p = 6.1e-05) comes from today’s demo transcript; the synthesis’s departures-from-guide section names the three places this build is weaker than the guide’s bar (no live human user, no hosted deployment, terminal transcript instead of video).

Test count 121 → 126. Version 0.2.0. The FDE layer added ~0 product risk (the platform core is untouched) and, per the guide’s own framing, converted a strong evaluation product into a deployment-shaped portfolio artifact: the harness was already the artifact; now the agreement is written down.

Day 16 (v0.2.1 — Production Hardening, Multi-OS CI, and Web Dashboard)

With v0.2.0 established, the next phase addressed enterprise deployment readiness, cross-platform reliability (specifically on Windows environments and containerized runtimes), and visual observability for stakeholders:

• Windows Console Unicode Encoding (INC-9): Running agent-eval gate or make demo on Windows shells failed with UnicodeEncodeError: 'charmap' when rendering UTF-8 symbols (→, κ, box borders). Fixed by ensuring UTF-8 stream re-encoding in src/agent_eval_harness/cli/app.py and scripts/demo.py, setting PYTHONIOENCODING="utf-8" in CLI subprocess integration tests, and fixing a pytest test collection warning on TestCase dataclass by marking __test__ = False.
• Cross-Platform CRLF Dataset Hashing (INC-10): Git on Windows checked out dataset fixtures with \r\n line endings, causing dataset cryptographic SHA-256 verification to mismatch the Linux reference hashes in CI. Fixed root cause with repository-wide .gitattributes enforcing eol=lf, and added byte-level CRLF normalization (content.replace(b"\r\n", b"\n")) in dataset_sha256() inside datasets.py and test_runner_e2e.py. Result: byte-identical cryptographic verification invariant across all operating systems.
• Production Containerization: Created an enterprise-grade multi-stage Dockerfile based on python:3.12-slim, dropping privileges to a dedicated non-root user aeh (UID 10001). Added docker-compose.yml with preconfigured profiles for headless batch evaluation (runner), CI drift verification (status), and interactive browser visualization (dashboard). Added Makefile targets docker-build and docker-run.
• Zero-Dependency Web Dashboard (agent-eval serve): Implemented an embedded HTTP service (src/agent_eval_harness/web/server.py) using Python’s standard library http.server (zero extra dependencies). Provides visual pass-rate cards, baseline drift comparisons, failure taxonomy localization, step-by-step observable trajectory replay (think, plan, act, observe, final answer), and a REST API (/api/status, /api/runs, /api/benchmarks, /api/run) for triggering evaluations and inspecting raw JSON runs.
• Cross-Platform CI Matrix: Upgraded .github/workflows/ci.yml from Ubuntu-only to a dual-OS runner matrix: ubuntu-latest and windows-latest across Python 3.11 and 3.12. All jobs verified green on GitHub Actions.
• Suite Expansion: Added comprehensive unit tests in tests/unit/test_web_server.py. Test count increased from 126 → 132 tests, all passing.

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