Kjell Tore Guttormsen
b0b87570db
Status, seam-by-seam architecture, load-bearing test map, the executed S10 live run with committed artifacts, and the offline-suite invariant. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QdSfQdND84oeq2mbjueLTS
125 lines
7 KiB
Markdown
125 lines
7 KiB
Markdown
# portfolio-optimiser-claude
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Sibling implementation of the portfolio-optimiser method on the **Claude Agent SDK**
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(decision D7). An open, generic Python framework that finds cost savings *inside* each
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project in a portfolio of independent projects: agents generate candidate measures, a
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mandatory deterministic validator gates the numbers, domain experts judge via
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human-in-the-loop, and the system learns from the verdicts.
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> **Status:** the D7 build (S5–S10) is complete. The deterministic backbone, the agentic
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> loop, and the learning loop are wired seam by seam, each proven by load-bearing tests
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> (187 tests, all running offline without an API key). The programme's single budgeted
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> **live model run has been executed and validated** — its artifacts are committed under
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> [`runs/s10/`](runs/s10/) (see below).
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> **Disclaimer — technical framework only.** The deployer owns DPIA, risk assessment, and
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> the legal basis for any processing. The framework ships only the technical
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> preconditions: local-only operation, first-class provenance, no silent data egress.
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## Built from the spec, not the sibling
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The method itself is framework-neutral and lives in
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[`portfolio-optimiser-commons`](https://git.fromaitochitta.com/ktg/portfolio-optimiser-commons)
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(consumed here as a git subtree under [`shared/`](shared/)): the normative method spec
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(RFC 2119), the OKF bundle-navigation contract, the golden/conformance suite — the *only*
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oracle for the validator — and the shared expert-reviewer persona skill. This repo
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implements that spec on the Claude Agent SDK; it deliberately does **not**
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reverse-engineer the MAF sibling
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([`open/portfolio-optimiser`](https://git.fromaitochitta.com/open/portfolio-optimiser)).
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Two independent implementations of one spec, compared afterwards, is the point of D7.
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## Architecture — the seams
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Everything below the run layer is pure config/file logic and runs deterministically,
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offline. Module by module:
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**Deterministic backbone** (method-spec §3 step 4, §7–§10)
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- `ir.py` — the typed cost-IR of a candidate measure (§7.1).
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- `validator.py` — the deterministic validator; blocking, and frozen by the shared golden
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suite (§7.2), which is the only fasit it answers to.
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- `provenance.py` — the first-class provenance stamp (§9); authoritative data, not
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after-the-fact logging.
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- `contracts.py` — fail-fast startup contracts (§10): stop criteria and budget caps are
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required at startup, and the model map (`data/model_map.json`, role → Claude model id
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per backend profile) is validated before anything runs.
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**Context seam** (§3 step 1)
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- `okf.py` — read-context built by **navigating** the project's OKF bundle (`index.md` +
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frontmatter + cross-links, progressive disclosure) — never keyword chunk-stuffing. The
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`type: verdict` layer is excluded from the read-context.
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- `experience.py` — the ExpeL-style experience seam: store, structural retrieval, and the
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gated fold. A prior expert verdict reaches the next hypothesis *only* through the fold,
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never by leaking through context.
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**Agentic loop** (§3 steps 2–5, §8)
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- `budget.py` — the budget meter: no unbounded loop exists anywhere in the framework.
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- `loop.py` — generate, maker–checker debate, gate, and informed refinement: the
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validator's previous rejection reason is fed into the next bounded attempt, so the
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model corrects against the falsification instead of re-answering identically.
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**Learning loop** (§3 steps 7–8, §4–§6)
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- `inbox.py` — the async verdict-file contract: an expert drops a plain-JSON verdict into
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an inbox folder after a run; a later run ingests it tolerantly and merges it before the
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fold.
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- `promotion.py` — the promotion gate, **fail-closed**: only an approved verdict is
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lifted into the OKF context layer; anything else raises and writes nothing.
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- `persona.py` — the expert-reviewer persona sourced from the shared artifact in
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[`shared/skills/expert-reviewer/`](shared/skills/expert-reviewer/) at call time, so the
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shared persona is genuinely consumed and cannot rot silently.
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**Run layer** (the only part that touches the network)
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- `sdk_client.py` — the Claude Agent SDK client, isolated from local configuration
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(`setting_sources=[]`) so no user/project config can leak into a run.
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- `artifacts.py` — §9 citations plus deterministic run-artifact persistence, including on
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structured stops (a budget stop still leaves artifacts behind).
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- `run_s10.py` — the programme's ONE live run (cost discipline D6); run-path only.
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### Load-bearing tests (§11)
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Every seam is proven by a test that goes **red when the seam is detached** — green-but-dead
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is the failure mode the rule exists for. Among them: `test_step1_expel_loadbearing.py`
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(the verdict signal reaches the prompt via the fold, and only via the fold),
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`test_checker_gate_loadbearing.py` (an explicit checker reject blocks a validated
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proposal), `test_step5_refine_loadbearing.py` (the rejection reason verifiably reaches the
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retry prompt, and the loop still stops at the cap), `test_step7_async_loop_loadbearing.py`
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(a verdict dropped after run A reaches run B's prompt through the file loop, with an
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empty-inbox control), `test_step8_promotion_loadbearing.py` (the gate refuses non-approved
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verdicts; the promoted signal stays out of the read-context), and
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`test_sdk_isolation.py` (local config cannot capture the checker).
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## The live run — S10, executed and validated
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Where the MAF sibling proves its loop end-to-end with a scripted offline simulation, this
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repo's end-to-end proof is the programme's single budgeted **real** run (D6: exactly one
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live API run in the whole programme), executed 2026-07-03 against the micro bundle
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[`shared/examples/bygg-energi-mikro/`](shared/examples/bygg-energi-mikro/):
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- exit 0 · validator `validated` · checker `approve` on the first attempt · 2 of 12
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rounds · 36 791 of 150 000 budgeted tokens · **cost $0.127514** (Haiku 4.5, per the
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model map), under a first-class `max_budget_usd` cap.
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- The proposal claimed a deliberately conservative 30 000 NOK saving against the
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bundle's p10–p90 band of 68.5k–121k — and validates.
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- All four artifacts are committed as fixed reference output in [`runs/s10/`](runs/s10/):
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`proposal.json`, `provenance.json` (with §9 citations), `run_result.json`, `usage.json`.
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Honesty rule (§1): everything else in the repo is deterministic and offline; nothing here
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claims more live behaviour than that one documented run.
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## Stack
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Python ≥3.10 · [`claude-agent-sdk`](https://pypi.org/project/claude-agent-sdk/) ≥0.2
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(bundles the Claude Code CLI; an API key is needed only at actual `query()` time) ·
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`uv` · Pydantic for contract validation.
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## Development
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```bash
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uv sync # install dependencies
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uv run pytest # 187 tests — run without any API key and without network
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uv run ruff check . && uv run ruff format --check .
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uv run mypy src # strict
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```
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The offline invariant is deliberate: everything below the run layer is pure config/file
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logic, so the full suite (including every load-bearing seam proof) runs with no key and
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no network.
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