Implementing the Agentic Trust Framework: Zero Trust for AI Agents

Originally published on Berlin AI Labs

The Cloud Security Alliance released the Agentic Trust Framework (ATF) as a zero-trust security model for AI agents. It's an important spec. But it shipped as documentation only — no running code, no reference implementation, no way to test it against real agents. We decided to build one.

The Problem: AI Agents Have No Zero Trust

Traditional zero trust (NIST SP 800-207) was designed for human users accessing corporate resources. AI agents break every assumption in that model:

• They don't have sessions — they have continuous autonomous loops
• They don't access one resource — they chain 30+ API calls in sequence
• They don't have a fixed intent — they adapt actions based on intermediate results
• When they go wrong, they can exfiltrate data, inject prompts into other agents, and make irreversible decisions The ATF addresses this with 5 trust elements: | Element | Question | |:---|:---| | 🔐 Identity | Who is this agent? Who owns it? | | 👁️ Behavior | Is the agent doing what it claims? Can we prove it? | | 🛡️ Data Governance | What data goes in? What comes out? Is PII protected? | | 📊 Segmentation | Where can this agent go? What can it access? | | ⚔️ Incident Response | What happens when the agent goes rogue? | ## The Architecture: 12 Services, 5 Elements Our implementation isn't a monolith — it's 12 independently deployed services: | ATF Element | Service | What It Does | |:---|:---|:---| | Identity | Agent Trust Verifier | DID:web resolution, JWT-VC issuance | | Identity | Agent Trust Protocol | Reputation scoring, compliance tracking | | Behavior | Veracity Core | Ed25519 Proof of Execution, Solana anchoring | | Behavior | Agent Chain Anchor | Chain-agnostic blockchain proof anchoring | | Data Governance | ConvoGuard AI | Sub-20ms ONNX firewall — prompt injection, PII | | Data Governance | Agent Fairness Auditor | Bias detection, audit logging | | Segmentation | Segmentation Engine | Policy-as-code, rate limiting | | Segmentation | Agent Deadline Enforcer | SLA enforcement, breach detection | | Segmentation | Agent Semantic Aligner | Cross-domain vocabulary translation | | Incident Response | Agent Pentest | 41 adversarial vectors, Safety Score A-F | | Incident Response | ATF Incident Service | Circuit breaker, kill switch | Every service is open source (MIT), tested, and deployed. ## What the Spec Doesn't Cover (and We Built) ### 1. Maturity Model Runtime The spec describes maturity levels (Intern → Director) but doesn't define how an agent earns promotion. We built 5 Promotion Gates:

typescript
const gates = [
  { name: 'Performance',    check: accuracy > 0.9 && availability > 0.99 },
  { name: 'Security',       check: pentestGrade <= 'B' },
  { name: 'Business Value', check: roi > 0 && ownerApproved },
  { name: 'Incident Record',check: criticalIncidents === 0 },
  { name: 'Governance',     check: securityTeam && riskCommittee },
];
2. Segmentation & Policy Engine
Real-time access evaluation using agent maturity level, rate limits, and resource classification:

typescript
const result = segmentationService.evaluateAccess({
  agentId: 'supply-chain-optimizer-v3',
  resource: 'procurement-db/write',
  maturityLevel: 'intern',
});
// → { allowed: false, reason: 'Maturity level insufficient' }
3. Incident Response Circuit Breaker
Three-state circuit breaker (CLOSED → OPEN → HALF_OPEN) that auto-isolates agents exceeding failure thresholds.

Validate It Yourself
bash
git clone https://github.com/yogami/atf-reference-implementation.git
cd atf-reference-implementation
npm install
npm test
# → 25/25 contract validation tests passing
Links
📦 Reference Implementation
🎮 Interactive Demo
📄 ATF Spec
🔐 agent-pentest on npm — test your own agents
If you're running agents in production and need trust guarantees beyond slideware — happy to compare notes.

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