Loopgrid

Loopgrid is an evidence platform for AI agent decisions. It captures the full context of an AI action — which agent proposed it, what authority it had, which policies applied, who approved it, which tool ran, and what outcome actually occurred. The platform then seals that information into signed, append-only records that can be exported and verified independently. It's not another logging tool; it's built to preserve accountability, not just telemetry.

The system works alongside your existing stack. You don't need to rebuild around a new model or agent runtime. Loopgrid receives decision streams through SDKs and open protocols like MCP and OpenTelemetry, then commits each event to a workspace-isolated chain. Human approval steps are recorded too, so you can see exactly when a person signed off before a consequential action executed. The evidence bundle includes manifest.json, decision.json, events.jsonl, and a public key, making it possible to verify the record outside the Loopgrid UI.

This is most useful for teams building AI agents that take real-world actions — processing refunds, modifying accounts, or calling external APIs. It also helps compliance, audit, and incident response teams who need to prove what an AI did and why. If you want accountability for your AI systems without replacing your existing tooling, Loopgrid gives you a straightforward way to keep trustworthy evidence.