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Kinetic Real-time entropy monitoring + kill switches for autonomous AI agents

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Kinetic is a safety observability platform for LangChain, CrewAI, AutoGen, and custom autonomous AI agent fleets. Stop expensive runaway agents before they burn your credits. Key Features Live LLM entropy & integrity scoring (detects hallucinations and drift in real time) Smart auto kill-switches (70% warning → 85% auto-kill threshold) Atomic fleet-wide "KILL ALL" with proper transaction safety Rich alerts feed: Jailbreaks, Cost Spikes, Tool Misuse, Tone & Toxicity, Drift . Execution Trace Explorer with latency breakdowns Configurable policy engine (Anti-Jailbreak Basic/Aggressive, Cost Guardrail, PII Filter, Latency SLA, etc.) Webhooks, detailed telemetry, and SDKs (Python + Node.js) Live Demo Try it now ⬇️ Closed Beta Offer Join the beta and get 3 free agent slots with Direct founder support included. Who is it for? AI engineers and teams running production or heavy-testing autonomous agents who want reliability and cost protection. Currently in closed beta with active development based on real user feedback. Artificial Intelligence, Developer Tools, SaaS
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Useful direction. For kill-switch tools, I would test that every stop decision is replayable without depending on the dashboard still being alive:

- trigger input/source ref

- policy and threshold version

- entropy/integrity score snapshot

- tool/action pending at kill time

- actor: warning, auto-kill, human kill-all

- post-kill state and what was not executed

- verdict for the run: matched policy, drifted, or unverifiable

The beta failure cases I would want are false-positive kill during a long but valid tool call, policy update between run and review, missing trace segment, and PII redaction hiding the evidence needed to review the stop. I am working on a receipt layer around this same problem and would be happy to cross-test a small external fixture if useful.