Trajeckt: a fail-closed gateway that enforces what AI agents can do (~1.6ms)

Trajeckt, a new fail-closed gateway, enforces AI agent actions across entire sequences of tool calls rather than inspecting them one at a time, blocking violations that span multiple steps before irreversible actions fire. The system operates below the model at runtime with sub-2 millisecond latency, integrating as a configuration change for any stack using MCP or OpenAI-compatible tool calls. Trajeckt turns agent executions into a structured evidence layer for live feeds, policy state, replay history, and learning signals, enabling governance and observability across the full trajectory.

A single tool call is rarely the problemThe sequence is. Every agent guardrail today inspects actions one at a time — so the failures that only exist across steps walk right through. Trajeckt enforces commitments across the whole trajectory, at runtime, below the model, before any irreversible action fires. Learn how your agents think Trajeckt turns executions into a structured evidence layer: live feeds, policy state, replay history, and the learning signals that shape better runs. Flip it. The plan doesn't change. The outcome does. Go ahead. It's a replay. The injected step was off the declared plan, so it was blocked before it fired: the same tool, allowed in plan and stopped out of it. 1.7msScripted from benchmark run-20260604-152135 One Enforcement Layer. Everything else Follows. Trajeckt turns executions into a structured evidence layer: live feeds, policy state, replay history, and the learning signals that shape better runs. Integration. A gateway, not a library: if your stack speaks MCP or OpenAI-compatible tool calls, enforcement is one config change. Any MCP client. The highlighted line is the server URL — the only change. Turn every trajectory into operating intelligence. Start with a control surface built for agent observability, governance, and investigation. Trajeckt makes it easier to see what happened, enforce what should happen, and improve what happens next.