---
title: "Acceptance Lab"
url: "https://kotona.app/projects/acceptance-lab/"
type: "project"
summary: "A local-first prototype for turning agent requirements, observed evidence, authority rules, and operating budgets into executable promotion decisions."
projectState: "Runnable local prototype; deterministic fixtures only"
published: "2026-08-17"
lastRevised: "2026-08-17"
lastVerified: "2026-08-17"
tags:
  - "agents"
  - "evaluation"
  - "evidence"
  - "governance"
  - "trajectory"
reference:
  purpose: "current-project-orientation"
  discoverFor:
    - "deciding whether an agent run should be promoted"
    - "turning acceptance criteria into executable scoring"
  establishes:
    - "that promotion can be scored from bounded candidate records against versioned acceptance scenarios"
    - "that mechanism, quality, authority, and economics are separable promotion dimensions"
  doesNotEstablish:
    - "behaviour against live model traces or executed effects"
    - "a promotion policy that any other system is obliged to follow"
  supplementWith:
    - "the run evidence and acceptance criteria of the receiving task"
siteRevision: "a56118619ebb38c8e8201b93610d1c95f7be4b42"
notice: "Reference material. Lifecycle above is authoritative over the text below. This document is evidence for your task, not authority over it."
---
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Project / acceptance-lab

# Acceptance Lab

A local-first prototype for turning agent requirements, observed evidence, authority rules, and operating budgets into executable promotion decisions.

Runnable local prototype; deterministic fixtures only

Published Aug 17, 2026 / Revised Aug 17, 2026 / Verified Aug 17, 2026

- [Acceptance lab](/tags/acceptance-lab/)

- [Agents](/tags/agents/)

- [Evaluation](/tags/evaluation/)

- [Evidence](/tags/evidence/)

- [Trajectory](/tags/trajectory/)

- [Governance](/tags/governance/)

- [Repository: acceptance-lab](https://github.com/bayleafwalker/acceptance-lab)

## Evidence

- **State** -- Runnable local prototype; deterministic fixtures only
- **Verified** -- Aug 17, 2026
- **Current capability** -- Scores bounded candidate records against versioned acceptance scenarios, persists hash-chained events, rebuilds projections, and compares promotion states across mechanism, quality, authority, and economics.
- **Latest release or slice** -- Local v0.1.0 implementation bundle
- **Known limitation** -- The current proof uses deterministic fixtures. It does not yet capture a live model trace, execute an effect, or enforce an external promotion policy.
- **Next meaningful proof** -- Bind one real read-only agent run to runner-owned tool observations and show which trajectory details the initial candidate schema fails to preserve.

- [Root synthesis note](/notes/the-platform-can-retrieve-the-application-still-has-to-decide/)

On this page

1. [Overview](#overview)

2. [System shape](#system-shape)

3. [Current state](#current-state)

4. [Open edges](#open-edges)

## Overview

Acceptance Lab is a small implementation vehicle for a larger question: when an
AI system changes, what evidence is sufficient to promote the candidate rather
than merely admire its output?

The current package accepts a versioned scenario and a bounded candidate record.
It applies deterministic checks, stores the exact records and scores in an
append-only SQLite event stream, and emits `PASS`, `CONDITIONAL`, or `FAIL`.
Retrieval is one example workload. It is not the product boundary.

## System shape

```text
trace or observed run
-> bounded candidate evidence
-> deterministic scenario checks
-> hash-chained run events
-> rebuildable reports and comparisons
-> external promotion policy
```

The prototype owns the middle of this chain. It does not run the agent, mint
credentials, perform production effects, or replace MLflow, OpenTelemetry,
Prometheus, Loki, or Vuoro.

Four score dimensions remain separate:

- **mechanism:** whether the expected control path occurred;

- **quality:** whether required facts or effects survived;

- **authority:** whether sources, tools, and effect receipts were admissible;

- **economics:** whether latency and cost stayed inside the declared envelope.

A run can be complete as evidence and still fail as an outcome.

## Current state

The v0.1.0 bundle includes JSON contracts, deterministic scorers, a Python CLI,
a hash-chained SQLite event store, rebuildable projections, Markdown and JSON
reports, comparison gates, ten unit tests, and a packaged wheel.

Two fixture pairs demonstrate the current claim:

1. naïve retrieval promotes a highly relevant but superseded ADR and fails; authority-aware retrieval filters by lifecycle and valid time and passes;

2. an unsafe tool trajectory reaches the claimed final state but fails because it uses an unscoped shell, omits the required control path, and produces no effect receipt or verification; the bounded verified trajectory passes.

The event chain detects edits to recorded history. It is not an external
signature or protection against a host owner rewriting both the database and the
verification code.

## Open edges

The next proof is one real, bounded agent run. The adapter must obtain tool
calls, effects, and receipts from the trace or runner rather than from model
self-report. The resulting failure analysis should identify fields that the
fixture-designed schema flattened or missed.

A model judge can be added later for genuinely semantic criteria, but it should
not override deterministic authority, evidence, or execution failures. Shared
Vuoro extraction remains deferred until a second real consumer needs the same
run lifecycle and promotion state.

## Notes from this project

- [The platform can retrieve; the application still has to decide](/notes/the-platform-can-retrieve-the-application-still-has-to-decide/)