What does LatchLens actually do for users? @RialoHQ When an AI agent is given permission to access APIs, spend funds, call tools, or handle user data, even one weak policy rule can create a serious risk. Reading a long JSON policy manually does not always reveal what an agent could still misuse. That is where LatchLens helps. A builder can paste a Latch policy into LatchLens or begin with a sample policy. LatchLens then runs 14 adversarial scenarios against it, including identity theft, unsafe endpoints, excessive token limits, unapproved model escalation, PII exposure, overspending, rate-limit abuse and destructive methods. Instead of only saying ''the policy failed,'' LatchLens shows the first-deny trace where the request should have been blocked, which rule failed to stop it and what needs to change. This makes debugging agent permissions much faster and easier to understand. The basic workflow is: Add or select a Latch policy Run the local attack simulation Review the security score and exposed cases Inspect the first-deny trace Apply the suggested hardening Run the tests again and confirm the improvement Users can also generate a deterministic SHA-256 proof report, verify a downloaded report locally against the exact policy and optionally anchor its commitments on Rialo DevNet. This makes later tampering detectable and gives builders independently verifiable evidence of what was tested. If a Latch token is available, LatchLens can also compare safely reproducible cases with Latch’s live authorization endpoint. The token is not included in the report and is cleared after use. So, LatchLens is not another agent wallet or policy engine. It is a policy assurance and proof layer that helps builders answer a simple but important question: “Before I give this agent real authority, have I actually tested what its permissions allow?” Try it: https://t.co/5S54a2dgFv @JEAMSETH07