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v0.1.0 — First public release: the measurement layer for the Agent Capability Economy

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@roy-tong roy-tong released this 16 Aug 14:12
· 58 commits to main since this release
v0.1.0
9a414b7

Superseded by v0.1.1. The original release contained several measurement-model issues (reported 42/126 observations, an unverifiable "no self-reported numbers" claim, and a non-reproducible demo); all are documented and fixed in the changelog. History is kept, not deleted.

The measurement layer for the Agent Capability Economy

AgentMeasure is an open standard + reference implementation for measuring how AI agents discover, choose, use, and derive value from software capabilities — Reach → Choice → Use → Utility → Value.

This is the first public release. It ships the pieces needed to start instrumenting real agent-facing software today, with strict honesty about what is and isn't proven yet.

What's in this release

Standard (Draft 0.4.3)

  • Core specification: canonical observation schema, 6 payload types, validation, qualification resolution — standard/CORE.md
  • Metrics registry: 14 metrics with a single source of truth and bidirectional consistency checks — registry/metrics.yaml
  • Measurement integrity: observe-first policy, caller identity graded declared → correlated → attested (never guessed), validity defaults to unknown until evidence upgrades it

SDK

  • @agentmeasure/mcp v0.1.0 — TypeScript SDK for MCP servers, built on the official MCP SDK. Fail-open, zero-content by default, missing-call accounting — sdk/

Product (reference implementation)

  • Canonical end-to-end pipeline: adapters → canonical observations → collector → metrics — product/
  • local-analytics.py: six classes of trustworthy metrics, verified end-to-end with 42 calls / 126 observations / 0 schema rejections (synthetic traffic, honestly labeled)
  • Measurement Report #001 — first measurement, with every limitation stated

Docs

Design principles

  1. Observe first, qualify later. We default to unknown, not "this was production agent usage." Only evidence upgrades it.
  2. No self-reported numbers. Measurement happens at the callee boundary, so callers can't inflate their own stats.
  3. Not a payment rail, marketplace, or ranking. We standardize the economic facts those systems need — measurement before monetization.

Get involved

  • Report where our definitions break: Issues
  • Debate metric semantics: Discussions
  • Build on the SDK and tell us what you measure

MIT licensed. Built in the open, in public.

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