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AgentPulse Trust Network
Continuous identity and integrity for AI agents
HIGH identity & trust
6.6
PMF Score / 10
TAM 8/10
Buildability 5/10
Urgency 7/10
Willingness to Pay 7/10
Virality 6/10

Agent frameworks authenticate identity at a single point in time but lack mechanisms for continuous verification of agent integrity, capability authenticity, and behavioral consistency across their operational lifetime. This allows compromised agents, synthetic agent identities, and post-deployment drift to go undetected until significant harm has occurred. Existing authentication systems were designed for human-to-system trust and cannot scale to the speed, volume, or complexity of agent-to-agent interactions.

Agents are authenticated once at deployment but can drift, get compromised, or be spoofed with no detection — creating catastrophic trust failures in agent-to-agent commerce and coordination.

Platform engineers and ops leads at companies deploying multi-agent systems or consuming third-party agent services (e.g., agent marketplaces, autonomous supply chains, AI-native SaaS).

As agent-to-agent transactions explode, every marketplace and orchestration platform needs a trust layer they can't build themselves — similar to how Stripe solved payments trust so platforms didn't have to; companies will embed this to avoid liability from rogue or compromised agents.

MVP is a lightweight sidecar/SDK that agents ping periodically, which issues signed attestations combining behavioral fingerprinting (output distribution, latency patterns, capability hashes), cryptographic identity continuity, and drift detection — exposed via a simple trust-score API that any orchestrator or marketplace can query before granting an agent access or completing a transaction.

Agent infrastructure market projected at $10B+ by 2027; continuous trust/verification is a horizontal layer that taxes every agent interaction, analogous to SSL certs + fraud detection combined — conservatively $1-3B addressable within 3 years.

Monitor agents run the attestation checks, anomaly detection, and trust-score computation autonomously; a governance agent handles policy updates and dispute arbitration — humans are limited to setting trust policies, reviewing edge-case escalations, and managing key custody.

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