Trust Protocols: Standard protocols for managing multi-agent AI teams at scale.

What it does

Trust Protocols (operating under the brand Mnemom) provides a system for declaring, monitoring, and verifying the behavior of AI agents running in teams. The core offering consists of cryptographically signed protocols that track what agents are permitted to do and what they actually do at runtime. The system uses four main components: an Alignment Card that declares agent boundaries, an Integrity Protocol that verifies reasoning in flight, a governance layer (CLPI) that anchors policy, and a screening gateway (AEGIS) that checks every model call. The result is a signed, tamper-evident record of agent behavior.

Who it is for

The product targets teams building multi-agent AI systems that need compliance tracking and governance. The homepage emphasizes audiences like boards, auditors, and regulators—suggesting the tool is for organizations where AI agent behavior must be auditable and defensible. Founders building complex agentic workflows, particularly those facing compliance requirements, appear to be the primary audience.

Pricing

The site does not show prices.

How it stands out

The product treats agent governance as a cryptographic problem rather than a logging problem. Instead of recording what happened after the fact, Mnemom enforces policy before actions execute. The use of Ed25519 signing and portable Trust Ratings suggests the system produces machine-verifiable, shareable proof of agent compliance. The emphasis on "signed records, not promises" positions this as distinct from traditional monitoring tools. The five-layer architecture (Alignment Card, Integrity Protocol, governance, screening gateway, and trust ratings) is presented as coherent and purpose-built rather than assembled from separate tools.

What a founder should check

A rival builder should verify whether existing orchestration platforms (Anthropic's tool use, Langchain's agent frameworks, or similar) will quickly add equivalent governance layers, making standalone adoption difficult. Second, test the actual overhead of the signing and verification system—if it materially slows multi-agent workflows, adoption may stall in performance-sensitive use cases. Third, examine switching costs: once agents and their policies are declared and signed within Mnemom, how easy is it to export that configuration to a competing system, or does the Ed25519 signing lock customers in?

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