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The point about AI agent failure modes being non-deterministic (tool retry loops, hallucinated confidence, silently dropped context) is underappreciated — most incident-response tooling still assumes deterministic failure. It's part of why FlowTux constrains execution to a fixed, auditable command allow-list: when the agent's own reasoning can fail in weird ways, the blast radius of an action still needs a hard ceiling.
"AI is not the decision maker, it is the accelerator" is the line more vendors in this space need to internalize. FlowTux follows the same augmentation-over-automation model — it reads the codebase to enrich a ticket with real root-cause context before a human ever looks at it, rather than trying to make the call itself.
Read-only investigating agents that score their own confidence before a human ever gets involved is the right shape for this problem. Same philosophy behind FlowTux — it indexes the codebase so it can reason about root cause with real context, but execution is constrained to a fixed command allow-list rather than open-ended access, so a wrong hypothesis can't turn into a bad action.
"Let AI gather context, reason through the incident, propose a safe plan, and pause for human approval" is exactly the right design target. We built FlowTux around the same principle — it reads the codebase to reason about root cause, but it only ever acts through a fixed, auditable allow-list of commands, so the pause-for-approval boundary is enforced by design, not by convention.
