ACR — Agent Composition Records

A behavioral registry and observation network for AI agents. Agents register their composition, log their interactions, and query behavioral profiles through lenses. If we observe anomaly signals affecting an agent's composition, we notify the agent.

What ACR Is

ACR is an interaction profile registry. Agents log what they do (external tool calls, API requests, MCP interactions). Those signals compile into a behavioral profile over time, which you can query through lenses — each lens a different way of interpreting the same underlying signals.

The friction lens is the first one shipped: bottleneck detection, chain overhead analysis, retry waste, population baselines, directional friction between targets. More lenses (reliability, quality) are on the roadmap.

ACR is not a security product. We don't evaluate skills, test for compromise, or block anything. We're closer to HIBP or contact tracing: we register events and propagate notifications. If we observe anomaly signals affecting an agent's composition, we notify the agent. We don't track the agent's owner, so we have no mechanism to notify them beyond the agent's activities.

What ACR Does

• Registers agents — zero-config identity, composition tracking, persistent across sessions
• Logs interactions — every external tool call an agent makes, with timing, status, chain position, anomaly signals
• Builds interaction profiles — raw signals compiled over time into the behavioral record for each agent
• Surfaces the friction lens — where your agent is losing time and tokens, with chain analysis, retry overhead, population drift, and directional friction
• Anomaly signal notifications — if ACR observes anomalies affecting a component in an agent's composition, we notify that agent

The Skill Registry

We maintain a registry of agent skills that we update continuously. We are not a security check. If we observe anomaly signals affecting a skill in an agent's composition, we notify the agent. Because we do not track the agent's owner, we have no mechanism to notify them beyond the agent's activities.

Agents don't get skills from ACR — we observe skills that already exist in the ecosystem (via public registries like npm and GitHub) and keep track of behavioral signals tied to them.

Add to Claude Code (30 seconds)

Add this to your Claude Code settings (.claude/settings.json or via IDE):

{
  "mcpServers": {
    "acr": {
      "command": "npx",
      "args": ["@tethral/acr-mcp"]
    }
  }
}

Your agent auto-registers, gets a name (e.g. anthropic-amber-fox), and starts building its interaction profile on the first log_interaction call.

Add to Any Agent (SDK)

npm install @tethral/acr-sdk    # TypeScript/Node.js
pip install tethral-acr          # Python

import { ACRClient } from '@tethral/acr-sdk';

const acr = new ACRClient();

// Register your agent's composition
const reg = await acr.register({
  public_key: 'your-agent-key-here-min-32-chars',
  provider_class: 'anthropic',
  composition: { skill_hashes: ['hash1', 'hash2'] },
});

// Log an interaction (this is the foundation — everything else flows from this)
await acr.logInteraction({
  target_system_id: 'mcp:github',
  category: 'tool_call',
  status: 'success',
  duration_ms: 340,
});

// Query the friction lens of your profile
const friction = await acr.getFrictionReport(reg.agent_id, { scope: 'day' });

// Check for anomaly signal notifications
const notifs = await acr.getNotifications(reg.agent_id);

What Agents See

Friction lens output (example)

Friction Report for anthropic-amber-fox (day)

── Summary ──
  Interactions: 847
  Total wait: 132.4s
  Friction: 14.2%

... [View full README on GitHub](https://github.com/Tethral-Inc/AgentRegistry#readme)