- Python 100%
14-step plan composing the four Fase 1 spikes into a src/ vertical slice (debate -> blocking validator -> two-layer HITL + provenance -> ExpeL learning), deterministic-core-first, on real chat clients in both profiles. Grounded in the 3 research briefs + installed-1.9.0-source introspection (7 exploration agents). Adversarial review: plan-critic REVISE (3 blockers/7 major/4 minor) -> all addressed; scope-guardian ALIGNED (0 creep, 9/9 criteria mapped, 6/6 Non-Goals honored). Key revisions: in-process retriever-as-tool MVP path (mcp dep conditional/GA-only); two-layer HITL capture + stable Verdict.id minting; extend_instructions retired by a REAL SessionContext test; TextSpan ownership + wave re-ordering; budget None-as-hard-fail with synthetic-usage test double; self_repair token-bound in the generate loop (validator.py frozen); global stop-on-failure rule. gemini-bridge Pass 2 unavailable (MCP SDK broke). plan-validator strict 0 errors. brief research_status pending to complete. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Fif1r1En5W542HbZV88yMH |
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| CHANGELOG.md | ||
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portfolio-optimiser
Generic, open framework on Microsoft Agent Framework (MAF) for finding cost-savings / efficiency proposals within each project of a portfolio of independent projects. Multiple agents collaborate to generate candidate proposals; a mandatory deterministic validator (solver + Monte Carlo) decides the numbers; domain experts review via human-in-the-loop, and the system learns from their verdicts.
Status: Early development (plan phase). Not yet usable.
Disclaimer — technical framework only. This project is a technical framework. Organizations that deploy it are themselves responsible for ensuring a valid processing purpose and for any required assessments (DPIA, risk/ROS, security reviews, etc.). The framework ships technical affordances (local-only mode, provenance/audit logging, no silent data egress) to enable compliant use, but makes no compliance guarantees.
Design philosophy
The result will never fit any single customer 100%. The goal is a ~90% genuinely generic core plus clear extension points, so competent people can configure the last mile per customer. We deliberately do not chase the final 10%.
Docs
docs/research/2026-06-23-prior-art-platform.md— prior-art & platform research (incl. implementation register §15).docs/plan/2026-06-23-incremental-plan.md— incremental delivery plan.
Stack
Python ≥3.10 · MAF (agent-framework) · uv. Backend profiles: Azure/Foundry (full) + local (fallback).
Develop
uv sync
uv run pytest
uv run ruff check .