Kjell Tore Guttormsen
8814a698c2
Closes the honest Fase 2a limitation: docs_dir==bundle_dir let keyword
chunk-stuffing leak the verdict's realization rate ("0.82") into the debate /
generation prompt regardless of the ExpeL fold (it surfaced from both
verdict-led-fro.md AND golden.json). The realization signal now reaches the
hypothesis prompt ONLY via the gated ExpeL fold.
- okf.py: bundle_context() + Bundle.context_files render the navigated bundle
(index + frontmatter + cross-links) as the agent read-context, EXCLUDING
type: verdict (maalbilde §2/§4). Pure stdlib, still MAF-free.
- datasource.py: bundle_citations() derives first-class citations from the
navigated non-verdict files.
- run_project: on the bundle path context + citations + debate tools come from
navigation (tools=[]; navigation replaces query-time RAG); the road path keeps
chunk-stuffing unchanged.
Load-bearing (maalbilde §7): the marker is upgraded from the minted verdict id
to the realization signal itself. The empty-store control now asserts "0.82"
reaches NO prompt — RED against the pre-2b chunk-stuffing path, green after
navigation (TDD red->green). New okf-level test_bundle_context_excludes_verdict_layer
guards the seam directly.
Suite 133->134 passed, 4 skipped; mypy + ruff check clean. Reverted unrelated
ruff-format drift (backends/budget/verdicts/test_contracts) to keep the diff
surgical.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MHR8iKxJRxDiDfNw8HZmWE
3.6 KiB
portfolio-optimiser
Generic, open framework on Microsoft Agent Framework (MAF) for finding cost-savings / efficiency proposals within each project of a portfolio of independent projects. Multiple agents collaborate to generate candidate proposals; a mandatory deterministic validator (solver + Monte Carlo) decides the numbers; domain experts review via human-in-the-loop, and the system learns from their verdicts.
Status: Early development. The deterministic backbone is solid; the agentic learning loop is being wired one load-bearing seam at a time — Step 1 (OKF-navigated context → hypothesis) is wired, with the verdict layer kept out of the read-context (see below). Not yet end-to-end usable.
Disclaimer — technical framework only. This project is a technical framework. Organizations that deploy it are themselves responsible for ensuring a valid processing purpose and for any required assessments (DPIA, risk/ROS, security reviews, etc.). The framework ships technical affordances (local-only mode, provenance/audit logging, no silent data egress) to enable compliant use, but makes no compliance guarantees.
Design philosophy
The result will never fit any single customer 100%. The goal is a ~90% genuinely generic core plus clear extension points, so competent people can configure the last mile per customer. We deliberately do not chase the final 10%.
Agentic loop — wiring status
The mandatory deterministic backbone (validator + budget meter + provenance) is solid and load-bearing. The agentic learning loop (see the target picture §11) is wired one seam at a time:
- Step 1 — OKF context → hypothesis (wired).
run_project(..., bundle_dir=...)wires the first agentic seam in two halves:- Context by navigation, not stuffing. The agent read-context is built by navigating the project's OKF bundle (
index.md+ frontmatter + cross-links, progressive disclosure) — never keyword chunk-stuffing (target picture §2/§4). - Verdict layer gated out of context. The
type: verdictlayer is excluded from that context; the candidate's prior expert verdicts reach the hypothesis prompt only through the gated ExpeL fold (folded in before generation), so a prior verdict provably — and exclusively — influences the next hypothesis. - Two load-bearing tests fail when the seam is detached: the realization signal must reach the prompt via the fold (
tests/test_step1_expel_loadbearing.py), and must never leak via context (tests/test_okf.py::test_bundle_context_excludes_verdict_layer).
- Context by navigation, not stuffing. The agent read-context is built by navigating the project's OKF bundle (
- Steps 3–8 (checker gating, informed refinement, async file feedback, gated wiki promotion) — not yet wired.
Docs
docs/plan/2026-06-26-maalbilde-agentic-loop.md— target picture: the agentic cost-saving loop + OKF knowledge architecture (north star).docs/research/2026-06-23-prior-art-platform.md— prior-art & platform research (incl. implementation register §15).docs/plan/2026-06-23-incremental-plan.md— incremental delivery plan (deterministic backbone).shared/— framework-neutral shared core (concept + example OKF knowledge bundles), reused unchanged by both reference implementations.
Stack
Python ≥3.10 · MAF (agent-framework) · uv. Backend profiles: Azure/Foundry (full) + local (fallback).
Develop
uv sync
uv run pytest
uv run ruff check .