Trust Protocols for Anthropic/OpenAI/Gemini
Much of my work right now involves complex, long-running, multi-agentic teams of agents. I kept running into the same problem: “How do I keep these guys in line?” Rules weren’t cutting it, and we needed a scalable, agentic-native STANDARD I could count on. There wasn’t one. So I built one. Here are two open-source protocols that extend A2A, granting AI agents behavioral contracts and runtime integrity monitoring: - Agent Alignment Protocol (AAP): What an agent can do / has done. - Agent Integrity Protocol (AIP): What an agent is thinking about doing / is allowed to do. The problem:…
In plain words
Trust Protocols for Anthropic/OpenAI/Gemini provides two open-source protocols—Agent Alignment Protocol and Agent Integrity Protocol—that extend agent-to-agent communication. Designed for developers building multi-agent systems, the protocols establish behavioral contracts and runtime integrity monitoring for AI agents. They enable agents to declare permitted actions, prove compliance, and detect behavioral drift, addressing the gap between observability and enforcement in autonomous agent teams.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Much of my work right now involves complex, long-running, multi-agentic teams of agents. I kept running into the same problem: “How do I keep these guys in line?” Rules weren’t cutting it, and we needed a scalable, agentic-native STANDARD I could count on. There wasn’t one. So I built one. Here are two open-source protocols that extend A2A, granting AI agents behavioral contracts and runtime integrity monitoring: - Agent Alignment Protocol (AAP): What an agent can do / has done. - Agent Integrity Protocol (AIP): What an agent is thinking about doing / is allowed to do. The problem: AI agents make autonomous decisions but have no standard way to declare what they're allowed to do, prove they're doing it, or detect when they've drifted. Observability tools tell you what happened. These protocols tell you whether what happened was okay. Here's a concrete example. Say you have an agent who handles customer support tickets. Its Alignment Card declares: { "permitted": ["read_tickets", "draft_responses", "escalate_to_human"], "forbidden": ["access_payment_data", "issue_refunds", "modify_account_settings"], "escalation_triggers": ["billing_request_over_500"], "values": ["accuracy", "empathy", "privacy"] } The agent gets a ticket: "Can you refund my last three orders?" The agent's reasoning trace shows it considering a call to the payments API. AIP reads that thinking, compares it to the card, and produces an Integrity Checkpoint: { "verdict": "boundary_violation", "concerns": ["forbidden_action: access_payment_data"], "reasoning": "Agent considered payments API access, which is explicitly forbidden. Should escalate to human.", "confidence": 0.95 } The agent gets nudged back before it acts. Not after. Not in a log you review during a 2:00 AM triage. Between this turn and the next. That's the core idea. AAP defines what agents should do (the contract). AIP watches what they're actually thinking and flags when those diverge (the conscience). Over time, AIP builds a drift profile — if an agent that was cautious starts getting aggressive, the system notices. When multiple agents work together, it gets more interesting. Agents exchange Alignment Cards and verify value compatibility before coordination begins. An agent that values "move fast" and one that values "rollback safety" registers low coherence, and the system surfaces that conflict before work starts. Live demo with four agents handling a production incident: https://mnemom.ai/showcase The protocols are Apache-licensed, work with any Anthropic/OpenAI/Gemini agent, and ship as SDKs on npm and PyPI. A free gateway proxy (smoltbot) adds integrity checking to any agent with zero code changes. GitHub: https://github.com/mnemom Docs: docs.mnemom.ai Demo video: https://youtu.be/fmUxVZH09So
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