Skip to content

Writing

The AI-Augmented Consultant: Knowledge Infrastructure Before Deliverables

A brief arrives on Friday afternoon. A compliance startup is building an AI-powered scoring engine. They have a working architecture. What they do not have are settled positions on five hard architectural questions. The deadline is Monday.

The question I asked myself was not "how do I answer these five questions?" It was "what knowledge infrastructure do I need to answer them well?"

That reframing is the entire methodology in one sentence.

KCP v0.1 to v0.5: How a Knowledge Standard Grows

KCP (Knowledge Context Protocol) has gone from a draft proposal to a v0.5 spec in one week. This post walks through what each version added, why those decisions were made, and where the spec is heading next.

The short version: every release promotes optional fields from community RFCs into the normative core. The spec is a strict superset at each step — a manifest written for v0.1 is still valid under v0.5.

The Evolution of KCP: From Minimal Draft to AI Knowledge Standard — v0.1 through v0.5 and Synthesis v1.20.0 implementation overview

What Happens When an AI Submits a PR and Another AI Reviews It

Recursive Refinement

We submitted a pull request to CrewAI adding a KCP manifest and TL;DR summary files. The goal was straightforward: contribute the same efficiency improvement that cut agent tool calls by 76% in our benchmark. Open it up, share the result, see if the maintainers want it.

What happened next was not what I expected.

KCP on Three Agent Frameworks: Same Pattern, Bigger Numbers

Five repos benchmarked, 73–80% reduction across three major AI agent frameworks. AutoGen leads at 80%, CrewAI at 76%, smolagents at 73%.

Today we applied KCP to three of the most widely-used AI agent frameworks — smolagents (HuggingFace, 25K stars), AutoGen (Microsoft, 55K stars), and CrewAI (44K stars). All three got the same treatment: a knowledge.yaml manifest, pre-built TL;DR summary files for the highest-traffic sections, and a before/after benchmark using the same model and methodology.

The results: 73%, 80%, and 76% reductions in agent tool calls. Open PRs are live on all three repositories.

73% smolagents (HuggingFace), 80% AutoGen (Microsoft), 76% CrewAI — KCP applied to these three widely-used AI frameworks yielded identical patterns of massive tool-call reduction.

KCP on Two Repos, Two Days: What the Numbers Actually Show

KCP benchmarking: 119 → 31 tool calls on application code, 53 → 25 on documentation. Two case studies, same methodology.

This week we applied KCP to two repositories back to back. Both got a knowledge.yaml manifest, pre-built TL;DR files for the highest-traffic sections, and a before/after benchmark using the same model and methodology.

The repos are very different. One is an application codebase — a plugin wizard for an AI-native design platform, 15 documentation units covering architecture, agent types, tools, shape schemas, and plugin protocols. The other is a pure documentation repository — a 13-chapter production guide for building safe infrastructure agents, 226 KB of structured decision frameworks and deployment checklists.

The question was whether KCP adds meaningful value in both cases, and whether the nature of the content changes the answer.

Without a manifest, agents wander. With one, they go straight to the answer.