Kjell Tore Guttormsen
65e4f5d9e4
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
79 lines
4.2 KiB
Markdown
79 lines
4.2 KiB
Markdown
# The Concept
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*A plain-language description of what this project is and why it matters — written for a
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non-specialist, e.g. a business developer at another company. Framework-neutral: it
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describes the method, not any particular implementation.*
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---
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## The problem
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Many organizations run a **portfolio of independent projects** at the same time —
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construction projects, IT services, infrastructure works, production lines. Inside *each
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individual* project sit hidden cost savings: a material that could be substituted, a
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specification that is needlessly conservative, a procurement contract that could be
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renegotiated. Finding them requires an experienced specialist to study that specific
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project in depth — and that expertise is expensive and scales poorly across a whole
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portfolio. So the savings are left on the table.
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Generative AI can suggest ideas, but a business developer immediately sees two obstacles:
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you cannot trust numbers a language model *guesses*, and the model does not know your
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industry's actual rules and experience. The concept is built precisely to remove these
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two obstacles.
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## How it works
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For each project, everything known about it is gathered — project documents, the
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discipline's assessment methodology, relevant professional literature, and the hard
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constraints (budget, what cannot change, regulatory requirements) — into a **curated,
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per-project knowledge base**. It is built on an open, vendor-neutral standard (Google's
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Open Knowledge Format), so it is portable and not locked to a single vendor.
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A team of AI agents reads this context, proposes concrete measures, and debates them
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against each other — one proposes, another critiques. But — and this is the heart of it —
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**the agents are never allowed to decide the value themselves.** Every number is sent to a
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separate, **deterministic calculation engine** (mathematical optimization plus risk
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simulation) that computes the actual saving. The AI proposes; the math decides. That is
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the trust anchor that separates this from "ask a chatbot."
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The measures that survive the calculation are presented to a **human domain expert** who
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issues a verdict: approve, improve, or reject.
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## What makes it valuable over time
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This is where the differentiation lies. The system **learns from the experts' verdicts.**
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There is almost always a gap between what a model *computes* and what an experienced
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specialist actually *approves* — because the expert knows how measures behave in practice,
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not just in theory. The system captures that gap and feeds it back, so the proposals get
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sharper on *your* organization's reality, not generic averages. The more verdicts, the
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better.
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And it respects how experts actually work: sometimes they respond on the spot, other times
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it takes days or weeks. The expert simply places their assessment into a **folder**, and
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the system picks it up whenever it arrives. No requirement for real-time, and no one has to
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be standing by.
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## What it is — and isn't
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It is a **purely technical framework**, not a finished compliance product. The
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organization that adopts it owns its own purpose, privacy, and governance; the framework
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only provides the technical preconditions (run locally, traceability on every proposal, no
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data leaving silently). It can run locally or in the cloud, and is published openly so
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others can adopt it.
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## Why it is built twice
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The same concept is built on **two different AI agent platforms** — Microsoft's Agent
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Framework and Claude's Agent SDK — on exactly the same example, so they can be compared
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fairly. This gives both an open reference others can copy, and an honest basis for judging
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which platform fits the task best.
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## Concretely
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Consider energy efficiency across a portfolio of buildings. Each building is a project with
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its own measure options (ventilation, lighting, insulation). The calculation engine
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computes the *modeled* saving for a package of measures under a budget. But an energy
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advisor knows that modeled and realized savings are rarely the same — behavior, measurement
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uncertainty, and interactions between measures create a gap. The expert's verdicts teach the
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system to close that gap over time. It is exactly in a field like this — where genuine
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expert judgment exists *beyond* pure calculation — that the concept comes into its own.
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