GuardClaw – cryptographically verifiable execution logs for AI agents
Cryptographically chained audit logs for AI agents; offline verification means no SaaS lockdown.
EPI (Evidence Packaged Infrastructure) packages AI execution as evidence.
Turns an agent run into a verifiable .epi bundle you can hand to auditors or replay locally for debugging. Concrete engineering choices stand out — crash-safe SQLite WAL storage, Ed25519 sealing, and an embedded viewer — though wider integrations (Kubernetes/CICD hooks, verifier tooling) and stronger ecosystem docs will be needed for real adoption.
ML infra engineers, platform teams running AI agents, security/compliance engineers
EPI is a portable, cryptographically sealed artifact format (.epi) for AI agent execution.
Problem: When AI systems run in production and something goes wrong, there’s no tamper-proof way to prove exactly what happened.
EPI records execution steps, inputs/outputs, metadata, and signatures into a verifiable bundle that can be replayed and audited.
It’s open-source and installable via pip.
I’d love feedback from: – ML infra engineers – Platform teams running AI agents – Security engineers
Happy to answer any technical questions.
Cryptographically chained audit logs for AI agents; offline verification means no SaaS lockdown.
Standardizes portable cryptographic receipts for agent behavior—but adoption unclear, overlaps Nobulex heavily.
OAuth + TLS for AI agents with Ed25519 identity and global kill switch before agents act.
OAuth + TLS for AI agents—eight-step verification pipeline, but adoption depends on framework integration.
Agent OAuth, but the problem isn't mainstream enough to matter yet.
Offline artifact verification with signed governance, but what threat model does this solve?