portfolio-optimiser

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

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.

Solo-maintained, fork-and-own. This is a starting point, not a vendor product. One maintainer, no SLA, MIT licensed. Issues are welcome as signals; pull requests are not accepted. See the organisation governance for the full model — including what to adopt instead if you need vendor accountability.

AI-generated: all code produced by Claude Code through dialog-driven development, with human review, test and judgement before anything ships.

A short visual introduction — 11 slides, in Norwegian, for a general audience — ships with the repo: open docs/kort-presentasjon.html in any browser.

Install

Python ≥3.10, with uv. The package is not published to a package index — install it from source:

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

The clone is the documented path because the walkthrough below points at files in the tree. It is no longer a technical requirement for the DATA: a built wheel carries the shared spec, the persona skill and the example bundles under shared/ as packaged data, and the resolver prefers a working tree when one is present (PORTFOLIO_SHARED_ROOT overrides both) — so an installed distribution finds its knowledge without a checkout.

Installing a built wheel

A wheel is not installable on its own, and the failure is a resolver error rather than a missing file. Two dependencies are pinned to git tags, and [tool.uv.sources] is uv configuration that does not travel with wheel metadata — so the wheel names llm-ingestion-okf and llm-ingestion-guard as bare names that no package index can resolve. Supply the two requirements alongside the wheel (measured: 65 packages, exit 0):

uv pip install portfolio_optimiser-1.1.0-py3-none-any.whl \
  "llm-ingestion-okf @ git+https://git.fromaitochitta.com/open/llm-ingestion-okf.git@v0.3.2" \
  "llm-ingestion-guard @ git+https://git.fromaitochitta.com/open/llm-ingestion-pipeline-security.git@v0.3.4"

Both are tag-pinned deliberately: they are security components, and a version that can move under an install is a gate that can stop gating without a local diff. uv sync from a clone reads the pins from pyproject.toml, which is why the source path above needs none of this.

uv sync installs two commands: portfolio-optimiser (the CLI) and portfolio-optimiser-demo (the offline end-to-end proof). They are equivalent to the python -m portfolio_optimiser.run and python -m portfolio_optimiser.simulation forms used throughout this README, which keep working — the module form is spelled out below so a reader can see which module answers a given command.

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

uv run pytest

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

Walk the whole chain offline

Seven commands, no API key, no network, no cost. They exercise the real loop — context navigation over the knowledge base, the maker/checker debate, the deterministic validator, the verdict — with scripted stand-ins for the agents' answers. Every scripted invocation prints a banner saying so, because a scripted run that reads like a model run would be worse than having no offline mode at all. What this shows is that the loop closes and the gate bites; it does not show how well a given model would propose or judge.

1 — Look at the knowledge base. It is curated markdown, not a black box:

ls shared/examples/bygg-energi-mikro/

2 — Watch the learning loop close. Two runs separated by an expert approval, with the second demonstrably informed by the first:

uv run python -m portfolio_optimiser.simulation   # or: uv run portfolio-optimiser-demo

Each run shows the refinement step: the proposer's first claim is falsified by the deterministic validator, and the corrected claim validates. Between the runs, knowledge travels back on both feedback timescales, each carrying its own marker so neither can stand in for the other: the persona's approved verdict is promoted into the file-backed wiki (promote → re-seed → fold), and a second verdict — an operations note an expert drops into an inbox folder after the run, the way a reviewer would days later — is merged from disk into the next run's store before its hypothesis is formed (write file → merge → fold). Both markers are present in Run B's prompt and absent from Run A's. Nothing crosses in memory.

The run is anchored: the demo copies the example knowledge base and adds a cost-baseline.json — the project's actual cost lines — so the validator's first stage reconciles every cost line a proposal claims against them, before the solver runs. The declared baseline is printed at the top, because it is the one property the rest of the output looks identical with or without. Those numbers are synthetic, derived from the scripted replies themselves; a knowledge base that ships its own baseline replaces them through the same seam, with no code change.

3 — Run the loop over a knowledge base, with answers you supply. Write the stand-in replies, then point the CLI at the bundle:

cat > replies.json <<'JSON'
{
  "proposer": "{\"measure\":\"LED-retrofit\",\"affected_items\":[{\"code\":\"ENERGI-TOTAL-EL\",\"quantity\":300000,\"unit_cost\":1.0}],\"claimed_saving_nok\":30000}",
  "checker": "The numbers are within a feasible range. VERDICT: APPROVE"
}
JSON

uv run python -m portfolio_optimiser.run BYGG-KONTOR-NORD \
  --docs-dir shared/examples/bygg-energi-mikro \
  --bundle-dir shared/examples/bygg-energi-mikro \
  --scripted-replies replies.json

Ends in ValidatedProposal. Swap --bundle-dir/--docs-dir for your own bundle to run it over your own data — that is the point of this door, and the reason it is not the same thing as step 2. Your bundle needs one file the ingest layer does not write for you: validator-input.json, the candidate the deterministic validator judges (a bundle without it is refused, by design — see docs/extending.md). Copy the shape from shared/examples/bygg-energi-mikro/.

4 — Watch it say no. Raise claimed_saving_nok to 250000 in replies.json and run the same command again. The outcome becomes Rejection: the deterministic validator refuses a saving the project's own numbers cannot support, no matter how confidently the proposer asserted it. This is the part of the method that carries the weight — the agents propose, and something that cannot be argued with decides.

Read that summary line carefully: Rejection (verdict id=…, decision=approved) is not a contradiction. Rejection is the validator's outcome, while decision= echoes the human's recorded verdict — here the --decision default, since nobody reviewed this run. The two are deliberately separate: a machine gate that blocks, and a human judgement that approves, are different questions and are never collapsed into one field.

5 — See what it would cost with a real model, before spending anything:

uv run python -m portfolio_optimiser.costsim --projects 4 --profile local

Modelled upper bounds per role and model, with the source of each price quoted. --profile local prices the free local backend; the estimate is a ceiling, not a bill.

6 — Run the whole portfolio, and watch the gate anchor to each project separately. The same flag works across every bundled reference project at once:

cat > replies.json <<'JSON'
{
  "proposer": "{\"measure\":\"scope_reduction\",\"affected_items\":[{\"code\":\"01.1\",\"quantity\":1,\"unit_cost\":850000}],\"claimed_saving_nok\":40000}",
  "checker": "Rigging and site operations can absorb this reduction. VERDICT: APPROVE"
}
JSON

uv run python -m portfolio_optimiser.run --portfolio --scripted-replies replies.json

One ValidatedProposal, three Rejection. All four reference projects carry a cost line 01.1, but at four different amounts — so a claim stated against one project's estimate is refused for the other three. Nothing about the proposal changed between them; what changed is the project's own numbers, which is the whole point of anchoring the gate to a cost baseline rather than to the proposal's internal arithmetic.

The four lines quote the same verdict id=. That is not a bug: a verdict is keyed on the candidate it judges, not on the project it was judged in, so an identical proposal mints an identical id by design — that key is how a later run finds the earlier judgement.

A portfolio pass reports what happened to every project. Projects that raised are printed to stderr with their error, and the command exits non-zero; the projects that completed still print their outcome, because one dead project must not discard the rest of the pass. A pass stopped because a savings goal was reached says so too.

The global token cap is reported separately from a goal stop — running out of budget and hitting your target are not the same event — but note that the cap itself has no command-line flag yet: only a library caller can install one, so that line is unreachable from the CLI today.

7 — Report what has actually been realized:

uv run python -m portfolio_optimiser.run --report --ledger savings-ledger.json

This reads a savings ledger and prints per-project and portfolio totals with each entry's provenance. It makes no model calls and changes nothing.

The ledger is an input, and the framework will not write it for you. It records savings that were actually realized — a contract was changed, an invoice came in lower — which is a fact about the world, not a conclusion the system is entitled to draw from its own proposals. A validated proposal is a claim; a ledger entry is a result. Keeping them apart is deliberate, and it is why no command here produces a ledger as a side effect. Run --report before creating one and it says so plainly (run report refused: savings ledger not found).

You write entries when the saving materializes:

from portfolio_optimiser.ledger import LedgerEntry, SavingsLedger, to_ore

ledger = SavingsLedger()
ledger.add_realized(
    LedgerEntry(
        project_id="FV42-GSV-E1",
        dimension="rigg",
        candidate_identity="33fba649cade8529",
        amount_ore=to_ore(40000),
        verdict_id="33fba649cade8529",
        provenance="expert Kari Nordmann, 2026-08-05, realized via contract amendment",
    )
)
ledger.save("savings-ledger.json")

Amounts are held in øre as integers, and to_ore is the only conversion — money is quantized once, per amount, before anything is summed.

--live-dry-run is a different, narrower drill: it builds contracts, clients and budget against your own configuration and stops before the first model call. It verifies the setup; it does not run the loop. --scripted-replies runs the whole loop. The two are mutually exclusive and passing both is refused rather than one silently winning.

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. Ingested content passes a content gate (materialize_gated) that scans every generated concept with llm-ingestion-guard before any of it reaches the bundle; a refused run writes nothing. 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. 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. The falsifications that informed a later attempt are surfaced on the result (RunResult.refinements), so what the run corrected in response to is inspectable, not just what it ended up with.
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.

• Backends: a run binds to one of two profiles. local (the development default) talks to an OpenAI-compatible endpoint on loopback and makes no egress. azure talks to a Foundry project and resolves its configuration from the environment at call time:

  • Endpoint — PORTFOLIO_FOUNDRY_PROJECT_ENDPOINT first, then the FOUNDRY_PROJECT_ENDPOINT that Foundry injects into a hosted agent. Ours wins, so exporting it is always decisive; the injected name is what lets the same image run hosted with no extra wiring. Neither set is a fail-fast naming both.
  • Credential — AzureCliCredential on a developer host (constructing it acquires no token; az login stays your manual step), and ManagedIdentityCredential when FOUNDRY_HOSTING_ENVIRONMENT is present, because a hosted container has no Azure CLI and the platform mints it a dedicated Entra identity instead.

  Deployment names are never committed: the role→model map ships REPLACE-WITH-* placeholders that fail fast, and PORTFOLIO_MODEL_MAP points at an out-of-tree map that wins over the bundled one.

• 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, --live-dry-run, and --scripted-replies <file> (the offline whole-loop door — see Walk the whole chain offline; mutually exclusive with --live-dry-run, which stops before the first model call rather than answering it).
  • 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).

  # 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.

Hosted on Foundry Agent Service

The repo ships the three files a hosted-agent deployment needs. All three are measured — schema-validated and container-built — but an actual azd deploy has not been run; that step is the operator's.

• main.py — the ONE process entry. It serves the hosted-agent runtime contract (port 8088 / PORT, GET /readiness, POST /invocations, clean SIGTERM exit) from portfolio_optimiser.hosting, wrapping run_project — never Workflow.as_agent(), which would bypass the deterministic validator and the rest of the gate chain. No protocol library is used: the prerelease agent-framework-foundry-hosting requires agent-framework-core>=1.13.0 while this tree locks 1.9.0, so the contract is a small stdlib-asyncio server instead (single event loop, no threads — the same concurrency model as the portfolio waves).
• Dockerfile — python:3.12-slim-bookworm + git (the two security components are git-tag-pinned direct references; wheel metadata alone cannot install them) + uv sync --frozen --no-dev (the exact locked resolution every measurement ran against). Build it the way a stranger would: git archive HEAD | docker build --platform linux/amd64 -t portfolio-optimiser -
• azure.yaml — closed against the two authoritative JSON schemas (see docs/2026-08-13-fase4-azure-yaml-valg.md): language: docker, invocations protocol pinned to 2.0.0, no env: block (the platform injects FOUNDRY_PROJECT_ENDPOINT; model choice is config, not environment), no startupCommand (the image CMD is the one copy).

An invocation is a JSON object whitelisted onto run_project's signature — project_id, docs_dir and verdict_input required; bundle_dir, profile, max_rounds, max_tokens and top_k optional. Unknown fields are refused by name (400), never silently dropped. profile defaults to azure on this surface: the AZURE profile reads its endpoint and credential from the environment at call time, so the same image runs hosted (managed identity) and locally (az login) without rewiring.

Docs

• Bestille en kjøring (norsk) — for the domain expert who COMMISSIONS a run: naming the approaches the run must evaluate (and/or asking the system for its own), stating what the run is for, and reading the announcement it prints before spending anything and the settlement it prints afterwards. The commission directs what is evaluated, never what is approved.
• Ekspert-svar (norsk) — for the domain expert who has to deliver the verdict: where a judgement goes, what an approval, an approval-with-correction and a rejection actually look like, and paste-ready examples of each. Marked throughout as AI-authored and not verified professional judgement.
• Building a knowledge base — 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 — 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 .