failproofaiObservability plus policy enforcement for agent harnesses: records every run across 12 harnesses (Claude Code, Codex, Cursor, Hermes…) and blocks dangerous tool calls with 39 built-in policies.
Why switchBoth are runtime security for agents. Adrian watches actions and reasoning traces to catch prompt injection and tool poisoning; Failproof AI enforces named policies on tool calls across 12 harnesses and keeps the session record. Detection vs enforcement.
Full comparison → GuardrailsNVIDIA's programmable guardrails for LLM apps: input, output, dialog and retrieval rails defined in Colang, wrapping any model or LangChain runnable.
Why switchBoth police live LLM behavior, opposite philosophies: NeMo Guardrails executes rails you wrote in Colang — you enumerate the policy; Adrian infers the agent's remit and flags deviations, reasoning traces included.
Full comparison → numbatPerplexity's endpoint sensor for AI agents: hooks, OTLP logs and on-disk session artifacts normalized into one CEL-matched event model, with opt-in pre-action blocking and offline forensics.
Why switchBoth do runtime security for agents, with opposite epistemologies. Adrian reads the reasoning trace to catch injection semantically; numbat is deterministic — CEL rules over a normalized endpoint event model, emitting versioned NDJSON and case bundles built for an incident responder.
Full comparison → agentsightSystem-level observability for AI agents via eBPF and TLS tracing: correlates prompts and model calls with the processes, files and network the agent actually touched — no SDK, no proxy.
Why switchBoth look for agents misbehaving at runtime. Adrian inspects actions and reasoning for injection and tool poisoning; AgentSight audits the actual system effects — which files changed, which services got requests — from outside the agent.
Full comparison →