Kjell Tore Guttormsen
d6d83d42b5
Closes maalbilde §5 gap #1 (the one missing "feedback-into-prompt" dataflow) for the OKF-bundle path. Before, ExpeL was computed AFTER generation into a discarded SessionContext, so a prior verdict could not influence any hypothesis (context_providers=0). - New okf.py: framework-neutral OKF bundle navigation (index + frontmatter + cross-links), pure stdlib, no agent_framework/mcp (D7-portable), enforced by test_okf_is_maf_free. - verdicts.py: seed_store_from_bundle + bundle_candidate_features build the ExpeL substrate + the pre-hypothesis query key from a bundle. - run_project(bundle_dir=...): folds the candidate's prior verdicts into the generation context BEFORE generate_via_llm; the road path is unchanged. Load-bearing (maalbilde §7): test_step1_expel_loadbearing proves a prior verdict reaches the hypothesis prompt and goes RED when the fold is detached (shown via TDD red->green). The marker is the minted verdict id (content hash) because docs_dir==bundle_dir lets keyword chunk-stuffing leak the realization rate; clean layer separation is Fase 2b. Suite 121->133 passed; mypy + ruff check clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MHR8iKxJRxDiDfNw8HZmWE
3 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 context → hypothesis) is wired (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=...)navigates a project's OKF bundle and folds the candidate's prior expert verdicts (ExpeL retrieval) into the hypothesis prompt before generation — so a prior verdict provably influences the next hypothesis. Guarded by a load-bearing test that fails when the seam is detached (tests/test_step1_expel_loadbearing.py). - 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 .