docs(shared): framework-neutral concept note + shared-core contract

First artifact in the framework-neutral shared core (R1 effectuated). CONCEPT.md
is a plain-language business description of the method for a non-specialist
audience; README.md documents the shared-core contract (consumed unchanged by
both the MAF impl and the future Claude Agents SDK sibling; extract to commons
repo via git subtree split when sibling work starts). English per the repo's
documentation-language convention + open-publish intent.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019any9zfGNNwWJPX5Zq2QRz
This commit is contained in:
Kjell Tore Guttormsen 2026-06-26 21:26:45 +02:00
commit 65e4f5d9e4
2 changed files with 110 additions and 0 deletions

79
shared/CONCEPT.md Normal file
View file

@ -0,0 +1,79 @@
# The Concept
*A plain-language description of what this project is and why it matters — written for a
non-specialist, e.g. a business developer at another company. Framework-neutral: it
describes the method, not any particular implementation.*
---
## The problem
Many organizations run a **portfolio of independent projects** at the same time —
construction projects, IT services, infrastructure works, production lines. Inside *each
individual* project sit hidden cost savings: a material that could be substituted, a
specification that is needlessly conservative, a procurement contract that could be
renegotiated. Finding them requires an experienced specialist to study that specific
project in depth — and that expertise is expensive and scales poorly across a whole
portfolio. So the savings are left on the table.
Generative AI can suggest ideas, but a business developer immediately sees two obstacles:
you cannot trust numbers a language model *guesses*, and the model does not know your
industry's actual rules and experience. The concept is built precisely to remove these
two obstacles.
## How it works
For each project, everything known about it is gathered — project documents, the
discipline's assessment methodology, relevant professional literature, and the hard
constraints (budget, what cannot change, regulatory requirements) — into a **curated,
per-project knowledge base**. It is built on an open, vendor-neutral standard (Google's
Open Knowledge Format), so it is portable and not locked to a single vendor.
A team of AI agents reads this context, proposes concrete measures, and debates them
against each other — one proposes, another critiques. But — and this is the heart of it —
**the agents are never allowed to decide the value themselves.** Every number is sent to a
separate, **deterministic calculation engine** (mathematical optimization plus risk
simulation) that computes the actual saving. The AI proposes; the math decides. That is
the trust anchor that separates this from "ask a chatbot."
The measures that survive the calculation are presented to a **human domain expert** who
issues a verdict: approve, improve, or reject.
## What makes it valuable over time
This is where the differentiation lies. The system **learns from the experts' verdicts.**
There is almost always a gap between what a model *computes* and what an experienced
specialist actually *approves* — because the expert knows how measures behave in practice,
not just in theory. The system captures that gap and feeds it back, so the proposals get
sharper on *your* organization's reality, not generic averages. The more verdicts, the
better.
And it respects how experts actually work: sometimes they respond on the spot, other times
it takes days or weeks. The expert simply places their assessment into a **folder**, and
the system picks it up whenever it arrives. No requirement for real-time, and no one has to
be standing by.
## What it is — and isn't
It is a **purely technical framework**, not a finished compliance product. The
organization that adopts it owns its own purpose, privacy, and governance; the framework
only provides the technical preconditions (run locally, traceability on every proposal, no
data leaving silently). It can run locally or in the cloud, and is published openly so
others can adopt it.
## Why it is built twice
The same concept is built on **two different AI agent platforms** — Microsoft's Agent
Framework and Claude's Agent SDK — on exactly the same example, so they can be compared
fairly. This gives both an open reference others can copy, and an honest basis for judging
which platform fits the task best.
## Concretely
Consider energy efficiency across a portfolio of buildings. Each building is a project with
its own measure options (ventilation, lighting, insulation). The calculation engine
computes the *modeled* saving for a package of measures under a budget. But an energy
advisor knows that modeled and realized savings are rarely the same — behavior, measurement
uncertainty, and interactions between measures create a gap. The expert's verdicts teach the
system to close that gap over time. It is exactly in a field like this — where genuine
expert judgment exists *beyond* pure calculation — that the concept comes into its own.

31
shared/README.md Normal file
View file

@ -0,0 +1,31 @@
# shared/ — framework-neutral core
This directory holds the parts of the project that are **independent of any AI agent
framework** and are meant to be **shared, unchanged, between both reference
implementations**:
- **this repository** — the method built on Microsoft Agent Framework (MAF);
- **a sibling repository** (built later, in sequence) — the same method on the
**Claude Agents SDK**.
Sharing one identical core is what makes the two implementations a *fair comparison*:
both consume the same concept, the same example data, and the same expected outcomes,
so the only thing that differs is the agent framework itself.
## Contents (growing)
- [`CONCEPT.md`](CONCEPT.md) — the business concept, written for a non-specialist
(e.g. a business developer at another company).
- *(planned)* the method specification, the example knowledge bundles (OKF / LLM-wiki),
the expert-reviewer persona, and the golden-suite of expected validator outcomes.
## Rules
- **Nothing in here may import or depend on a specific agent framework.** If it does,
it does not belong in `shared/`.
- **Repo layout (decision R1, 2026-06-26):** the shared core lives here for now. When
work on the sibling repository begins, it will be extracted into its own repository
(e.g. `portfolio-optimiser-commons`) via `git subtree split`, and both implementation
repos will consume it. This defers cross-repo plumbing until it is actually needed.
See the target picture for the full architecture: `docs/plan/2026-06-26-maalbilde-agentic-loop.md`.