generate_via_llm consumed each validator Rejection internally (`last`), fed it into the next attempt's prompt, and dropped it. So Step 5 was real but unobservable: a caller could see THAT a proposal validated, never that it validated on attempt 2 after the deterministic validator falsified attempt 1. It was the one step of the eight with no output to show. The seam is a typed return value -- GenerationResult(outcome, refinements) -- rather than an out-parameter or a callback: a returned value cannot be silently lost by a caller that forgets to pass a collector, and mypy forces every call site to acknowledge it. refinements carries ONLY rejections that were actually fed back. When the attempt budget runs out the final rejection IS outcome; counting it here would be double-counting, and the bounded control test goes red on the collect-everything implementation that gets this wrong. The loop's bound is untouched: max_attempts and meter.tick_round stand, and `last` still drives the prompt alone, so prompt growth is unchanged. run.py accumulates across _evaluate calls, so _evaluate_mandate is untouched; RunResult.refinements defaults (the coverage precedent) and is concatenated across approaches rather than keyed per approach -- stated as an honesty limit. The simulation now shows it: the scripted proposer overclaims 250000, which the validator falsifies against P90 = 90000, and the corrected 30000 validates. Only the overclaim is scripted -- the rejection is computed. scripted_factory takes a per-role reply selector so this needs no second scripted client body. README records the two accuracy changes only (Step 5 is now inspectable; the simulation trace shows the correction). The level-2 publishing claim stays deferred until after the demo (O4). Load-bearing MEASURED against the full suite with a control, four mutations all red: detach the returned history (4 tests) - collect-everything (control only) - detach the run wiring (2 tests) - revert the simulation's proposer to a constant (the demo-protection test). Control: 759 passed / 4 skipped; ruff, format and mypy clean. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_017CcWFcREUi6YPjEpN3ACDP
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Changelog
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
[Unreleased]
Added
- Step 5 is now observable:
generate_via_llmreturns aGenerationResultcarrying the validator falsifications that informed a later attempt, surfaced onRunResult.refinements. The offline simulation exercises it — the scripted proposer overclaims, the deterministic validator falsifies the number, and the refined proposal validates.
Changed
- Breaking (library API):
generate_via_llmreturnsGenerationResultinstead ofValidatedProposal | Rejection; read.outcomefor the previous value. The refinement loop's bound is unchanged (max_attempts+ token meter). simulation.scripted_factoryaccepts a per-role reply selector over(prompt, role)as well as a constant reply, so a scripted role can answer differently on a later attempt.
[0.1.0] - 2026-08-06
First tagged release. There is no prior release, so the entries below describe what this version is, not what changed since a predecessor.
Added
- Deterministic backbone: mandatory blocking validator (solver + Monte Carlo against the shared golden suite), budget meter with hard fail-fast caps, provenance stamping.
- Agentic learning loop wired end to end, one load-bearing seam at a time (target-picture steps 1, 3/4, 5, 7, 8): OKF-navigated bundle context with the gated ExpeL fold, maker-checker debate where the checker gates the reasoning, informed refinement (previous rejection reason fed into the next bounded attempt), async verdict file inbox, and gated wiki promotion (fail-closed).
- Offline end-to-end simulation proving the learning loop closes with a scripted client
(
uv run python -m portfolio_optimiser.simulation) — plumbing proof, not live-model proof. - Framework-neutral shared core in
shared/: OKF concept + example bundle, golden validator suite, and the expert-reviewer persona as an Agent Skill. - CLI parity (S5.3):
run.pydrives the whole method from the command line in two modes — single-project (--dimension-config,--outbox-dir/--run-id, plus the already-wired--bundle-dir/--verdict-dir) and portfolio (--portfoliowith--goals/--ledger), the latter printing an observablegoal reached: …line when a savings goal is met. Adds a fail-fastload_dimensionloader and structured refusals (rc 1, no traceback) for misuse and mode-exclusivity violations. The prior-verdict fold is on the--bundle-dirpath only; a--docs-dir-only run is single-shot. - Value report (S5.4): a read-only
--report [--json] --ledger <file>surface onrun.pythat rolls up the accumulatedSavingsLedger— per-project + portfolio totals (dimension-free-deduped integer øre), flagged cross-dimension overlaps (each counted once), and per-entry provenance — as a human table or deterministic JSON. Makes no model calls; mode-exclusive (only--ledger/--jsonpermitted with--report, which requires--ledger). Honest scope boundary: the report core (value_report.py) now exists but is deliberately not wired intocostsim'skost_mot_verdiplaceholder — that cost-vs-value integration is a separate, deferred step. Thecostsimseam note was reworded from the stale "fylles av S5.4 verdirapport" to a truthful forward reference socostsim's own output no longer claims the wiring is done. - Semantic retrieval seam (S3.1): a new MAF-free
semretrieval.pyadds anEmbedder/Retrieverpair and aHybridRankerblending a numpy cosine term over the embedded feature triple (sorted cost codes, measure type, magnitude bucket) with the existing structural score, exposed as--semantic-retrieval. What ships is the seam, not better retrieval quality: the bundledFakeEmbedderis a deterministic sha256 projection with no semantics, so over a structural tie the order is deterministic but arbitrary. A real embedder is selected from a CLOSED registry via--embedder-config/build_embedder— deliberately never an import path, so a config file can never name arbitrary code to load. Off by default and additive: with no retriever passed the store delegates toStructuralRetriever, which reproduces the pre-seam ranking exactly, so the text-excluded default and every existing test are unchanged. The embedding excludesdescription, matchingsimilarity("text is ignored by design") and the verdict-id hash — so a flag-on run reads no surface text either, and a genuine expert verdict can no longer be outranked by the framework's own echo of the query. The ranker is passed PER CALL, never assigned to the caller's store, so the opt-in cannot outlive the run that asked for it. Accepted in both run modes; in single-project mode it requires--bundle-dirand--verdict-dirand is refused — never silently ignored — without them. Determinism, precisely: ranking order rests on the total order(-round(score, 9), id); the BLAS thread pins set before numpy is imported (VECLIB_MAXIMUM_THREADSfor Accelerate,OPENBLAS/MKL/OMPfor other backends) defend the narrower claim that vector artifacts are byte-identical across environments. Ships an optional, rebuildablevectors.npy+vectors.jsonlstore (byte-identical regardless of insertion order; fail-fast on a row/line mismatch; missing loads asNone) — an authoring primitive with no caller insrc/, offered to extenders likewrite_verdictandpromote_verdict. numpy is confined tosemretrieval.pyand never entersokf.py,retrieval.pyorshared/. - Cost-baseline anchoring of the deterministic gate (S4.0):
validate_proposal(..., baseline=...)reconciles everyaffected_itemagainst the project's realCostBaselinein a stage 0, before the solver — two independent rejections (unknown cost code; a real code whosequantity/unit_costfalls outside a configurable tolerance, default 5 %, of the baseline value). Before this, every stage reasoned only about numbers the proposal itself supplied, so an internally consistent hallucination cleared the whole gate. Validation, never repair: the proposal is rejected, never silently corrected to the baseline. The argument is optional (Nonereproduces the earlier behaviour) but both run paths set it — the road path always, the bundle path only when the bundle ships acost-baseline.json, since a pre-amendment bundle is legitimately unanchored. A baseline that exists but is malformed raises on both loaders rather than reading as "no baseline". Method caps are looked up in an injectableMETHOD_CAPSregistry, so a second measure type is data rather than an edit to the validator. - Deterministic stage-2 bound and band enclosure in the validator (S2.7).
- Per-candidate verdict keying (S3.2):
seed_store_from_bundlereads eachtype: verdictfile's own structural frontmatter (affected_codes/measure_type/claimed_saving_nok) instead of collapsing a multi-candidate bundle onto the single IR projection, where a verdict about candidate B scored a perfect structural match against candidate A's query. All three fields or none — a partial declaration raises rather than being merged with the bundle candidate, which would mint a key belonging to neither. Bundles that declare none fall back to the previous keying, so every pre-S3.2 seed is unchanged. - Portfolio concurrency (S3.3): wave-partitioned execution with a per-project snapshot and a
deterministic merge barrier, pinned by a concurrent-equals-sequential contract test;
k < 1is fail-fast. Failures are collected and the pass continues, surfaced asRunFailureslots rather than aborting the portfolio. Goal-stop is evaluated at wave boundaries, with the intra-wave semantics documented and pinned. - Global portfolio token cap (S3.4/F10):
PortfolioBudget+PortfolioMeterform one ledger across a whole portfolio pass — and, seeded from a spend file, across passes — while the per-runBudget/TokenMeteris unchanged. Three enforcement points: a startup refusal when the remainder cannot fund a single run; wave admission, where an unfundable project is never started (a project that is merely aborted has already cost calls) and the pass stops structurally with the completed runs preserved; and a pre-call guard inBudgetMiddleware, so a call the remainder cannot pay for is refused rather than made. Because a whole wave is checked against the same pre-wave remainder, admission reserves each member's requirement. Resource exhaustion is reported in its ownbudget_stopfield, never merged intostop_reason— a goal stop is success. The spend file is our own accounting state, so corrupt content raises instead of reading as zero. - Ingest transports (S2.2, S2.4): the MCP connector as a transport inside the http family, and a time-bounded default http transport in front of the pinned ingest library. A server on the ingest path must expose a zero-argument tool (the URL carries both coordinates), which is a deliberately separate seam from the in-run data-source demo server.
- Azure/Foundry offline preflight config gate (
preflight.py, S4.1). - Offline live-dry-run drill (
--live-dry-run, S4.2): walks the whole path up to the eager client build and stops before the first model call — zero chat calls. - Out-of-band HITL verdict routing CLI (
hitl.py, S5.1): code-prefix routing on the candidate id. - Expert-notification contract (
notify.py, S5.2): the declaredNotifierwithConsoleNotifier,FileNotifier(byte-deterministic JSONL), andWebhookNotifier, plus a fail-fastbuild_notifier; the webhook is the only egress point, fail-closed behind an explicit per-runallow_egressopt-in. Not auto-wired intorun.py. - Offline whole-loop CLI door (
--scripted-replies): drives the entire method over the caller's own data with no model budget, in both run modes. Resolved before the portfolio dispatch, because placing it after made--portfolio --scripted-repliessilently drop the flag and attempt four real model calls (measured). Mutually exclusive with--live-dry-run: both are offline and they contradict, so the combination is refused rather than letting one quietly win. - Run mandate (
mandate.py,--mandate): a domain expert commissions which approaches a run evaluates, the run announces what it will do before doing it, and it settles for that — every commissioned approach is evaluated and every one appears in a coverage report. A mandate is not a dimension (dimension.admitsfilters what may pass the scoping gate; a mandate directs what the run spends its attempts on) and not a goal (the numeric target keeps its single home inGoalContract/--goalsand is merely restated in the announcement). Two construction-time refusals: an empty commission, and a duplicate approach id — including one claiming the reservedown-proposal— since the id keys each coverage row and two rows under one key collapse silently. Pure module: pydantic + stdlib only, D7-portable, guarded MAF-free. - MCP servers as in-run tools (
mcp_tools.py,--mcp-config): concrete external servers become tools the agents can call during the debate. Opt-in and additive — with no config there are zero network calls and the tool list is unchanged. Three rules are load-bearing: an allowlist is required (an empty one would let the far end decide what the agents may call), every server and every permitted tool is named in the announcement before the first call (also without--mandate— no undeclared egress), and--live-dry-runopens nothing. A malformed config is refused rather than degraded to "no external services", which would make the announcement describe a run nobody configured. This is a separate seam from the ingest-path MCP transport above. - Per-approach outbox artefacts: a run commissioned to evaluate three approaches previously wrote one
proposal artefact, so only the selected approach could ever receive a verdict and the others taught
the learning loop nothing. Artefacts are now keyed
{run_id}-{approach_id}-*.jsonand carryapproach_idin the payload, with the join key(run_id, approach_id)— widening only the filename would not have worked, because the reader joins on therun_idfield read from file content. Two properties make them genuinely judgeable:verdict_idis minted per approach (so one delivered verdict cannot clear all three from the queue), andprovenance.validator_decisionfollows its own approach rather than the run's.verdicts.verdict_keyis the single public verdict-key rule. - External-call provenance: the egress declaration says what a run may contact;
ToolCallRecorderrecords what it did, landing onProvenanceStamp.external_callsread after the debate. It only observes —call_nextis always awaited, so a trace can never alter the run it traces. Only configured tools are recorded; logging in-process functions would turn the record into a false egress claim, and an empty list is therefore a positive statement that nothing outside the process was contacted. Attribution comes from our own config because MAF reports the bare tool name with no server prefix (measured, not assumed), so a name allowed by two servers is recorded unattributed rather than credited to the first match. Stated honesty limit: this is the call and its source — not evidence that the service's answer reached the proposal. docs/knowledge-base-recipe.md(S5.3, D-H item 1): the documented team process (technical + domain expert) for building a knowledge base, with the honest 1–2 week expectation.- Test suite: 755 passing tests (4 skips are live-provider-only). Every wired seam is covered by a
load-bearing test that goes red when the seam is detached, and each carries a recorded detach point
verified by mutation. That property is maintained by measurement, not by assumption, and this
release contains two recorded cases where it did not hold until it was checked: the S3.1
--semantic-retrievalflag was covered only below the CLI, so hardcoding it off atmain()level left the suite green; and four of the fiveBudgetExceededraise sites could report any value in theobservedfield without a single test noticing. Both are now pinned.
Fixed
Defects found and closed before this first tag. Nothing here was ever released; they are recorded because each one changed an invariant an adopter depends on.
- Money quantisation happens in one order, from one source:
ledger.to_oreis the framework's single NOK→øre conversion, applied per amount, after which integers are summed. Previously the goal baseline summed floats and quantised once while the ledger summed per-candidate integers, and the two orders met in exactly one place — the check that decides whether a portfolio pass stops early on a percentage goal. Measured divergence: three lines of60000.005NOK are18000003øre quantised first but18000001summed first, enough to flip a goal. Both call sites were fixed; a fix to only one survived the whole suite (measured). - The MCP stdio ingest transport now runs against a real server, and its error contract is repaired:
stdio_clientandClientSessionare each a task group, and anyio wraps everything leaving one in an exception group, so the module's ownIngestErrorreached callers as an exception group and never as the type the ingest door switches on. The owned error is unwrapped and re-raised; anything else is re-raised untouched. No canned-tool test could have caught this — the defect only appears once the code is actually run. - The MCP timeout composes with anyio's own cancel scope (
anyio.fail_afternested inside both task groups) instead ofasyncio.wait_forfrom outside, which cancelled the structure anyio owns and produced aBrokenResourceErrorinside an exception group rather than a timeout. The translation is narrowed byCancelScope.cancelled_caught, so only the scope that hit its own deadline earns the timeout code — an unconditionalexcept TimeoutErrorwould mislabel anyTimeoutError, since the builtin is alsosocket.timeoutandasyncio.TimeoutError.anyiois now a declared direct dependency rather than a transitive one. - Frontmatter scalars are unquoted by exactly one rule (
okf.unquote_scalar). - A non-finite embedding is refused rather than scored.
--embedder-configis refused when--semantic-retrievalis absent, rather than silently dropped.- The portfolio CLI no longer swallows its offline door or its failures.
- Constants cited in the live documentation are gated against the code, so a doc and the value it quotes cannot drift apart unnoticed.
Notes
- Licensed under the MIT License (see
LICENSE). - Scope boundary: this is a technical framework. The deployer owns DPIA, risk assessment and processing purpose; the framework ships only the technical preconditions (local-only operation, provenance, no silent egress).
- The
shared/directory is a git subtree ofportfolio-optimiser-commonsand is pull-only.