Kjell Tore Guttormsen
93608d008d
Measured before writing: the commands existed but were scattered across the
mode partition, and there was no path a newcomer could walk end to end. The
capability gap that made a complete offline walk impossible is closed in
3abc61b; this is the door onto it in the README.
Five steps, each RUN FROM A FRESH CLONE before being written down (git clone +
uv sync + uv run pytest -> 652 passed): read the knowledge base, watch the
learning loop close, run the loop with your own scripted answers, watch the
validator say NO, and price a real run before spending anything. Step 4 is the
one that was missing entirely -- the demo only ever showed a yes, and a
refusal carries far more weight than another approval.
Also documents that `Rejection (..., decision=approved)` is not a
contradiction: the first is the validator's outcome, the second echoes the
human's recorded verdict. Documented rather than changed -- altering a public
output format is the operator's call, not a side effect of writing docs.
`costsim` gets its first mention in the README at all; it was finished, tested
and completely invisible from the surface.
Also points the commons link at open/ (published 2026-08-04). NB: the
repo-standard v0.3.0 register still lists 19 repos and does not know that repo,
so pointing at the correct live URL now trips a false LINK-DEAD. Reported to
org-ops; the URL answers HTTP 200.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01GWsexbQjPo9rsV3aUE54ZS
339 lines
20 KiB
Markdown
339 lines
20 KiB
Markdown
# 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)
|
||
[](pyproject.toml)
|
||
[](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.
|
||
|
||
## Walk the whole chain offline
|
||
|
||
Five 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:
|
||
|
||
```bash
|
||
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:
|
||
|
||
```bash
|
||
uv run python -m portfolio_optimiser.simulation
|
||
```
|
||
|
||
The trace ends with the approved verdict's marker present in Run B's prompt and absent from Run A's
|
||
— knowledge crossing runs purely through the file-backed wiki (promote → re-seed → fold).
|
||
|
||
**3 — Run the loop over a knowledge base, with answers you supply.** Write the stand-in replies,
|
||
then point the CLI at the bundle:
|
||
|
||
```bash
|
||
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.
|
||
|
||
**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:
|
||
|
||
```bash
|
||
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.
|
||
|
||
> `--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. 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/open/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`, `--live-dry-run`, and
|
||
`--scripted-replies <file>` (the offline whole-loop door — see
|
||
[Walk the whole chain offline](#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).
|
||
|
||
```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 .
|
||
```
|