# 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 specified but not yet implemented. 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)](https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md)**
  (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/`](shared/), a git subtree of
  [`portfolio-optimiser-commons`](https://git.fromaitochitta.com/ktg/portfolio-optimiser-commons)):
  the business concept, the normative [method spec](shared/method-spec.md) and
  [ingest spec](shared/ingest-spec.md), 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** — hand-curated today; the specified ingest layer will
  materialize bundles from real sources (file catalogues/CSV, SQL, HTTP as an extension point)
  via a deterministic, schema-validated manifest that runs *before* the loop.
- **Run:** the CLI takes a bundle directory and a verdict inbox directory; 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](docs/plan/2026-06-26-maalbilde-agentic-loop.md) — the agentic loop + OKF
  knowledge architecture (north star).
- [Prior-art & platform research](docs/research/2026-06-23-prior-art-platform.md) (incl.
  implementation register §15).
- [Ingest target picture](docs/plan/2026-07-03-maalbilde-ingest-lag.md) — 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).

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