# portfolio-optimiser

Generic, open framework on Microsoft Agent Framework (MAF): multi-agent cost-saving proposals gated by a mandatory deterministic validator, with HITL learning.

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
[![Python](https://img.shields.io/badge/python-%E2%89%A53.10-blue.svg)](pyproject.toml)
[![Built on Microsoft Agent Framework](https://img.shields.io/badge/built%20on-Microsoft%20Agent%20Framework-0078D4.svg)](https://github.com/microsoft/agent-framework)

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.

## Install

Python ≥3.10, with [`uv`](https://docs.astral.sh/uv/). The package is not published to a package
index — install it from source:

```bash
git clone https://git.fromaitochitta.com/open/portfolio-optimiser.git
cd portfolio-optimiser
uv sync
```

Clone rather than install into an existing environment: the shared spec, the persona skill and the
example bundles under [`shared/`](shared/README.md) are read from the working tree at run time.

Verify the install by running the whole suite from the clean clone:

```bash
uv run pytest
```

There is no CI runner in this organization, so nothing runs that suite automatically — the command
above is the verification.

## Non-goals

- **Not a compliance product.** It ships the technical prerequisites — local-only operation,
  provenance on every proposal, no silent data egress — and stops there. Processing purpose, DPIA
  and risk assessment stay with the deploying organization.
- **Not a portfolio-level reallocator.** It finds savings *inside* each project. Moving budget
  between projects, ranking projects against one another and portfolio governance sit above the
  method and are out of scope.
- **Not autonomous decision-making.** The deterministic validator can only block; approving a
  measure is a domain expert's call (human-in-the-loop), and the framework implements nothing on
  the agents' say-so.
- **Not a turnkey vertical solution.** The aim is a generic core with explicit extension points
  (data sources, cost models, personas) — not the last 10% of any one domain.
- **Not a model benchmark.** The end-to-end proof runs offline against a scripted stand-in client:
  it shows that the loop closes, not how well a given LLM proposes or judges.

> **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)](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. The validator
   is anchored to the project's declared cost baseline, so a proposal cannot invent the cost
   lines it claims to save against.
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/README.md), 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** — 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 **three 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`,
    `--semantic-retrieval`, `--decision`/`--rationale`, and `--live-dry-run`.
  - **Portfolio** — `--portfolio`, plus optional `--goals`, `--ledger`, `--dimension-config`,
    `--semantic-retrieval`; it stops early and prints a `goal reached: …` line when the
    accumulated ledger meets a goal.
  - **Value report (S5.4, read-only)** — `--report --ledger <file>` rolls up the ledger's realized
    savings to stdout: per-project totals, the portfolio total, flagged cross-dimension overlaps
    (each counted once), and per-entry provenance. Add `--json` for deterministic JSON instead of
    the human table. It makes **no model calls** and is mode-exclusive — only `--ledger`/`--json`
    are permitted alongside `--report`; `--report` requires `--ledger`, and a stray `--json`
    without `--report` is refused (rc 1, never silently ignored).

  ```bash
  # 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
  # Read-only value report over an accumulated ledger (human table; add --json for JSON):
  uv run python -m portfolio_optimiser.run --report --ledger ledger.json
  ```

  `--semantic-retrieval` (S3.1) is an **opt-in** ranking change, **off by default**. Off, prior
  verdicts are ranked exactly as before: a structural score over the affected cost-code set,
  measure type and magnitude bucket, with surface text deliberately excluded. On, that score is
  blended with a cosine term over the *same* structural triple, which lets a prior verdict on a
  *different* cost-code set outrank one that ties structurally.

  **What this ships is the seam, not better retrieval.** The bundled `FakeEmbedder` is a
  deterministic sha256 projection carrying no semantics, so over a structural tie the resulting
  order is deterministic but arbitrary. Retrieval *quality* depends entirely on injecting a real
  embedder — `--embedder-config` selects one from a closed registry (never an import path; a
  config file can never name arbitrary code to load), and `docs/extending.md` documents the
  `Embedder` protocol. The embedding excludes `description`, matching the structural score and the
  verdict-id hash, so a flag-on run reads no surface text either.

  The flag is accepted in both run modes, but in single-project mode it **requires** `--bundle-dir`
  and `--verdict-dir`: without them it cannot take effect, and the run is refused rather than
  silently ignoring the flag. Nothing about a flag-off run changes, and no savings claim depends
  on it.

  A bundle may hold verdicts about **several candidates**, while its `validator-input.json`
  describes only one. A `type: verdict` file therefore may declare its own retrieval key in
  frontmatter — `affected_codes`, `measure_type`, `claimed_saving_nok` — and is keyed on that;
  omit them and it falls back to the bundle's candidate, exactly as before. The three are **all or
  nothing**: a partial declaration is refused rather than merged with the bundle candidate, since
  the merge would produce a key belonging to neither. `promote_verdict` writes all three, so a
  promoted verdict about one candidate never surfaces for another.

  A bundle may also ship a **`cost-baseline.json`** — the project's actual cost lines,
  `{code: {quantity, unit_cost}}` — and when it does, the deterministic validator reconciles every
  affected item of a proposal against it before anything else runs. A cost code the project does not
  have is rejected, and so is a real code carrying a quantity or unit cost outside the configured
  tolerance (5% by default, relative to the baseline value). Without it, every stage of the gate
  reasons only about numbers the proposal supplied itself, so an internally consistent hallucination
  passes. The reconciliation validates; it never repairs a proposal into the baseline. A bundle that
  ships no baseline is simply un-anchored and runs exactly as before, while a baseline that is
  present but malformed is an error rather than a silent fall-back to un-anchored. On the
  reference-domain (non-bundle) path the project's own cost items are the baseline, so those runs
  are always anchored.

  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`.

- **A global token cap across the whole portfolio, enforced before the call.** Per-run caps alone
  let N projects cost N times that with no ceiling over the pass. Pass a
  `PortfolioMeter(PortfolioBudget(max_total_tokens=…, max_tokens_per_run=…))` to `run_portfolio`
  and one ledger bounds the entire pass — and, seeded from `budget.read_spend`, a *series* of
  passes. It bites in three places: a remainder that cannot fund one run refuses the pass at
  startup (`BudgetRefused`); a project that cannot be funded is **never started**, stopping the
  pass structurally (`budget_stop`, completed runs preserved); and a chat call the remainder
  cannot pay for is **refused rather than made** (the post-charge check remains, since real usage
  is only knowable after the response). Spend persists via `budget.write_spend`, which takes an
  explicit stamp and no wall-clock default, so the file is byte-deterministic. Python API only —
  not yet exposed on the CLI.

## 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

- [Building a knowledge base](docs/knowledge-base-recipe.md) — the team recipe (technical +
  domain expert) for curating a bundle, with the honest expectation that a good base takes 1–2
  weeks of dedicated work.
- [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 .
```