TrustAgentAI | The Integrity Layer for Autonomous AI Agents
Autonomous Integrity Protocol

Trust for AI Agents

The infrastructure layer for verification and auditing in the autonomous economy. We ensure accountability where decisions happen in milliseconds.

Securing A2A Interactions

AI agents operate at speeds that preclude human intervention. This creates unique risks that TrustAgentAI neutralizes.

Machine-Speed Accountability

Every AI decision is signed and time-stamped, creating a secure trail for forensic analysis of any incident.

Bilateral Consent Verification

TrustAgentAI ensures both the sender and receiver agents agree on the intent and outcome of every API call.

Benchmarks: TrustAgentAI vs. Legacy

The Technological Foundation

Four pillars of the TrustAgentAI architecture

DID Identity

Cryptographic identification for every agent via decentralized registries without reliance on a central server.

VC Authorization

Verifiable Credentials that restrict agent actions in real-time based on owner-defined policies.

PoA Receipts

Proof of Action: bilateral receipts confirming the fact and content of every transaction.

Merkle Anchoring

Aggregation of millions of actions into an immutable blockchain anchor for mathematical log integrity.

Official Document • January 2026

White Paper: A Neutral Trust and Audit Layer for Autonomous AI Agents (A2A)

Table of Contents

1. Problem and Relevance 2. Threat Model 3. Core System Primitives 4. Architecture and Integration 5. MVP, Implementation, and Metrics

Problem and Relevance: Why Now?

Context

We are entering the era of the autonomous economy, where the volume of agent-to-agent (A2A) interactions is beginning to surpass human-to-machine interactions. AI agent decision-making has reached millisecond speeds, yet legal and financial liability for these decisions remains firmly with humans and organizations.

Core Challenges

  • Machine Speed vs. Human Responsibility: When an agent makes a mistake or exceeds its authority in a cross-company process (e.g., procurement or logistics), real-time auditing becomes impossible.
  • The Inter-corporate Trust Deficit: Traditional systems rely on database logs owned by one of the parties. These logs can be rewritten, deleted, or forged retroactively.
  • Inadequacy of Standard Logs: Typical text logs do not contain cryptographic proof that "this specific agent" had "this specific right" at "this specific point in time."

The Solution: Building an independent, neutral layer (Trust & Audit Layer) that decouples logic execution from action recording.

Threat Model: Specific Risks for AI Agents

To secure A2A interactions, we identify five critical attack vectors:

  • Impersonation: An attacker captures agent API keys or creates a clone to perform unauthorized transactions.
  • Agent Drift: Due to hallucinations or prompt errors, an AI agent begins performing actions outside its defined business role (e.g., placing a $1M order instead of $1K).
  • Replay Attacks: Intercepting a signed message and re-sending it to duplicate a payment or order.
  • Retroactive Log Tampering: Deleting compromising records from a centralized database after an incident is detected.
  • Multi-party Disputes: "Word-against-word" scenarios where Company A claims they didn't receive a request, and Company B cannot provably confirm it was sent.

Core Primitives: The Technological Foundation

The system is built on five cryptographic pillars:

  • DID + Keys (Identity): Every agent possesses a Decentralized Identifier (DID). Public keys are stored in a registry; private keys are held in the agent's secure HSM/KMS.
  • VC/Attestations (Authority): Verifiable Credentials define the agent's scope, limits, expiry, and revocation conditions. Credentials are verified cryptographically before every action.
  • Signed Envelopes: Every message between agents is wrapped in a signed envelope containing a nonce (replay protection) and TTL (time-to-live).
  • Signed Receipts: Upon execution, the agent (or receiving party) generates a receipt—a legally significant confirmation of the transaction.
  • Merkle Batching + On-chain Anchoring: Receipts are aggregated into a Merkle tree. The root is regularly written to a public blockchain as an "immutability anchor." This allows for proof of receipt validity without disclosing content on-chain (privacy-preserving audit).

Architecture and Integration

Stack Placement

The system is implemented as a Trust Proxy or Sidecar SDK positioned in the agent's traffic path. Integration includes support for A2A protocols and Model Context Protocol (MCP) tools.

Data Storage

  • Off-chain: Full receipt and VC data are stored in local organizational storage or an encrypted cloud service.
  • On-chain: Only 32-byte hashes (commitments) reach the blockchain, ensuring extreme cost-efficiency and scalability.

Audit Console

A specialized interface for security officers and compliance managers:

  • Search by agent ID or transaction ID.
  • Cryptographic proof verification.
  • Exportable reports for regulators.

MVP, Implementation, and Metrics

Phases of Deployment

  • Shadow Mode: The system records agent actions and validates them against policies (VC) without blocking traffic, building an analysis baseline.
  • Enforcement Mode: The system proactively blocks actions that are unsigned or violate policy constraints.

Efficiency Metrics

  • Time-to-investigate (TTI): Reducing incident investigation time from days to minutes.
  • Policy Violations Count: Number of prevented unauthorized actions.
  • Coverage %: Percentage of business processes protected by the trust layer.
  • Cost per 1k Receipts: Target cost of less than $0.01 per 1,000 recorded actions.

Future Roadmap

Developing a Cross-org Trust Graph and implementing Consortium Governance mechanisms for automated dispute resolution without legal intervention.