# 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](docs/plan/2026-06-26-maalbilde-agentic-loop.md) §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](shared/examples/bygg-energi-mikro/) (`index.md` + frontmatter + cross-links, progressive disclosure) — never keyword chunk-stuffing (target picture §2/§4).
  - **Verdict layer gated out of context.** The `type: verdict` layer 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`).
- **Steps 3/4 — checker gates the reasoning (wired).** Two falsifiers now act on the same candidate: the deterministic validator gates the **numbers** (blocking, as before), and the maker-checker's checker gates the **reasoning** (target picture §2/§6). The checker ends its turn with a `VERDICT: APPROVE` / `VERDICT: REJECT — <reason>` line; an explicit reject blocks an otherwise-validated proposal (`run_project` surfaces both debate participants via `output_from=agents` and overrides the outcome to a checker-sourced `Rejection`). The gate is opt-in-reject (fail-open on a missing marker), and `provenance.validator_decision` stays honest — it reflects the validator only, never the checker. Load-bearing: `tests/test_checker_gate_loadbearing.py` goes red on *either* detach (revert `output_from`, or drop the override).
- **Step 5 — informed refinement (wired).** The proposer's bounded retry is no longer blind: the validator's *previous* `Rejection.reason` is fed into the next attempt's prompt (`generate.py` `generate_via_llm` → `_build_messages(prior_rejection=...)`), so the model corrects against the falsification instead of re-answering identically (target picture §5/§7). It carries only the most-recent reason (never an accumulated history, never the rejected proposal JSON) and runs under the *existing* `max_attempts` + token-meter cap — no new loop, so "improve until good enough" without a ceiling stays impossible. The only per-attempt falsifier here is the validator; seeding generation with the checker's critique is a run-level, separately-scoped concern and is deliberately not done here. Load-bearing: `tests/test_step5_refine_loadbearing.py` goes red when the reason is detached from the prompt (the outcome never flips *and* the verbatim-reason assertion fails), with a bounded control proving the loop still stops at `max_attempts`.
- **Steps 7–8** (async file feedback, gated wiki promotion) — not yet wired.

## Docs

- [`docs/plan/2026-06-26-maalbilde-agentic-loop.md`](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`](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`](docs/plan/2026-06-23-incremental-plan.md) — incremental delivery plan (deterministic backbone).
- [`shared/`](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

```bash
uv sync
uv run pytest
uv run ruff check .
```