portfolio-optimiser

A generic, open framework — built on Microsoft Agent Framework (MAF) — that finds cost savings inside each project of a portfolio of independent projects. A swarm of agents generates candidate measures; a mandatory deterministic validator (solver + Monte Carlo) decides the numbers; domain experts judge the outcomes (human-in-the-loop); and the system learns from their verdicts across runs.

Status: the full 8-step agentic loop is wired and proven with load-bearing tests, and the end-to-end proof is an offline simulation with a scripted stand-in client — no live-model run yet. The ingest layer (real data sources) is implemented — file/CSV and SQL on both stacks with bit-identical golden extractions from the shared spec, plus HTTP as a MAF-only demonstrated extension point against a local mock — but exercised only against committed fixtures: no bundle has yet been materialized from a live source. A sibling implementation of the same method on the Claude Agents SDK is built in parallel from the same shared spec.

Disclaimer — technical framework only. Deploying organizations own their processing purposes and assessments (DPIA, risk/ROS, security review). The framework ships the technical prerequisites — local-only mode, provenance, no silent data egress — but makes no compliance guarantees.

Built on an LLM wiki: Karpathy's idea, Google's format

The knowledge architecture is the heart of the project, and it is deliberately not ours:

• The idea is Andrej Karpathy's "LLM wiki": instead of pointing a model at documents written for people, you curate a small, versioned body of knowledge written for the model to read — concept files, explicit structure, explicit links.
• The format is Google Cloud's Open Knowledge Format (OKF) (open spec, v0.1), which formalizes that pattern: a knowledge bundle is a directory of markdown files with YAML frontmatter (one required field, type), a reserved index.md entry point, and intra-bundle cross-links forming an emergent graph. Custom frontmatter fields are allowed and must be preserved — which is exactly where this project's own layers (expert verdicts, ingest provenance) live.

Because OKF is open and vendor-neutral, the same bundles are consumed unchanged by both reference implementations (MAF and the Claude Agents SDK sibling) — the knowledge outlives any particular agent stack.

Not RAG. Agents read a bundle by navigating it — index.md first, then its cross-links, with progressive disclosure — never by keyword retrieval or stuffing the whole bundle into a prompt. Query-time retrieval against the bundle is explicitly forbidden by the method spec: it would leak the verdict layer around the learning gate.

AI-first, humans on top

A traditional wiki is built for people — optimized for humans finding and reading information, with machine access bolted on afterwards. This project inverts that order, and is a concrete example of what that looks like:

• The wiki (the OKF bundle) is written for the model: it is the agent's working memory and the substrate the learning loop reads from and promotes into.
• The human affordances are layers on top: experts judge outcomes by dropping a plain JSON verdict file in an inbox folder; an explicit, fail-closed promotion gate is the only path by which an approved verdict becomes wiki knowledge; reports and reviews are rendered from the machine-readable layers.

Humans stay decisive — nothing enters the wiki without an approval — but the primary reader of every file is the model, not a person browsing.

How it works

One run, one project, eight steps — with the learning loop closing across runs:

1. Understand — navigate the project's OKF bundle; fold the candidate's prior expert verdicts into the hypothesis prompt (ExpeL-style, retrieved structurally, never by text).
2. Hypothesise — one typed candidate measure (strict IR, fail-fast schema).
3. Debate — a maker-checker pair argues the reasoning (round-capped).
4. Validate — two falsifiers on the same candidate: the deterministic validator gates the numbers (blocking, never optional) and the checker gates the reasoning.
5. Refine — a rejected attempt retries informed by the rejection reason, under hard attempt and token caps. Unbounded loops are forbidden everywhere.
6. Propose or discard — a validated proposal with risk percentiles, or a typed rejection.
7. Expert feedback — days later, an expert drops a verdict file in an inbox folder; a later run picks it up. Fully resumable; no live session assumed.
8. Promote — an approved verdict is lifted into the wiki as a type: verdict concept file, navigable by the next run. The gate is fail-closed: raw agent output never self-promotes.

Every proposal carries provenance (citations into the bundle, model, validator decision, token usage). Every seam above is protected by a load-bearing test — a test designed to fail when the seam is detached, so the loop cannot silently degrade into theater.

How it is set up

• One shared, framework-neutral core (shared/, a git subtree of portfolio-optimiser-commons): the business concept, the normative method spec and ingest spec, the expert-reviewer persona as an Agent Skill, and an example bundle with a golden suite as the only ground truth. Both stacks implement from the spec alone.

• Per project: one OKF bundle — the bundled examples are hand-curated; the ingest layer that materializes a bundle from a source (file catalogues/CSV + SQL, HTTP as a MAF-only demonstrated extension point) via a deterministic, schema-validated manifest that runs before the loop is implemented and exercised against committed fixtures — no bundle has yet been materialized from a live source.

• Run: the run.py CLI has two modes — a documented partition, since one invocation cannot exercise every flag:

  • Single-project — PROJECT_ID --docs-dir <dir>, plus optional --bundle-dir, --verdict-dir, --outbox-dir (which requires --run-id), --dimension-config, --decision/--rationale, and --live-dry-run.
  • Portfolio — --portfolio, plus optional --goals, --ledger, --dimension-config; it stops early and prints a goal reached: … line when the accumulated ledger meets a goal.

  # Single-project, offline drill (builds contracts + clients, stops before the first model call):
  uv run python -m portfolio_optimiser.run FV42-GSV-E1 --docs-dir <docs> --bundle-dir <bundle> --live-dry-run
  # Portfolio run with a savings goal checked against an accumulated ledger:
  uv run python -m portfolio_optimiser.run --portfolio --goals goals.json --ledger ledger.json
  

  The prior-verdict fold — the learning step — happens only on the --bundle-dir path; a plain --docs-dir-only run is single-shot (no fold). --decision/--rationale apply to the single-project path only and are inert in portfolio mode. --outbox-dir must differ from --verdict-dir: writing the raw outbox into a folder later read as an inbox would re-ingest raw agent output past the promotion gate (self-contamination) — documented here, deliberately not CLI-enforced. Stop criteria and budget caps are required at startup. Try the offline end-to-end proof (no model, no network): uv run python -m portfolio_optimiser.simulation.

What this enables

The reference case is portfolio cost review (the example bundle is a building-energy measure), but the architecture is designed to generalize to any setting with the same shape — candidate measures inside independent projects, numbers a deterministic tool can check, and judgement only an expert has:

• Portfolio reviews — cost savings, energy efficiency, maintenance and procurement measures, proposed per project and validated against the project's own data.
• Compounding organizational memory — approved expert verdicts become navigable knowledge; the next run's hypotheses start from what experts actually decided, including realization gaps no solver can compute.
• Auditable AI — an unbroken provenance chain from expert decision back through proposal, bundle file and text span, and (with ingest) to the source system, query, and timestamp.
• Vendor-neutral knowledge — the same bundles drive two different agent stacks; switching frameworks does not orphan the organization's curated knowledge.

Docs

• Target picture — the agentic loop + OKF knowledge architecture (north star).
• Prior-art & platform research (incl. implementation register §15).
• Ingest target picture — connectors and the ingest layer (frozen 2026-07-03).

Stack & develop

Python ≥3.10 · MAF via the split GA packages (see pyproject.toml) · uv. Backend profiles: Azure/Foundry (full) + local (fallback).

uv sync
uv run pytest
uv run ruff check .