Evidence Gate sits between your agent and any consequential action. Before anything executes, the agent has to show fresh, authorized, non‑conflicting evidence that this specific call is justified — not just allowed.
Most agent stacks stop at the first question. That gap — between "allowed in general" and "justified in this instance" — is where agents take actions no one can later explain.
The agent has permission to call this tool. That permission was granted once, ahead of time, and doesn't know anything about the facts of this particular call.
This exact call is backed by evidence that is fresh, came from a source the agent was allowed to use, and doesn't conflict with anything else on record — checked at the moment it matters.
"Can we prove why this agent took this action, using facts it was actually allowed to rely on?"
Start in simulation with zero production risk. Move to live enforcement one tool at a time, on your schedule.
Import LangSmith, OpenAI, Langfuse, or custom traces. No agent changes, no production risk.
See which past actions would have been allowed, restricted, reviewed, or blocked — with reasons.
Draft readable YAML policy from your SOPs. A human approves before anything goes live.
Wrap one sensitive tool by name pattern or framework adapter. Fails closed by default.
Allowed actions carry a signed, short‑lived clearance token downstream services can check.
Every decision is hash‑chained, replayable, and exportable — without exposing raw prompts.
None of these are aspirational. They're the constraints the product is built inside of.
No LLM decides enforcement. Policies compile into explicit rules; decisions are reproducible.
Start with trace replay, then wrap existing tools with minimal code changes.
Sensitive tools require clearance. Downstream services can independently verify signed tokens.
Every decision is hash-chained, replayable, and explainable — without exposing raw prompts or payloads.
Works with LangChain, CrewAI, LlamaIndex, custom agents, remote services, and direct Python functions.
Humans inspect and approve rule packs. LLMs may help draft policy — never silently enforce it.
Needs a control point that doesn't require rewriting every tool integration.
Needs to answer why the agent did something, not just what it did.
Ships agents touching money or customer data, wants a guardrail they didn't have to build.
Needs tamper-evident, replayable logs — without access to raw prompts.
No rewrite, no production risk. Connect a trace export and see what Evidence Gate would have decided.