01
infinite loop
DETECT
the agent is repeating itself and going nowhere.
CAUGHT
on loop_detected → steer
MITIGATE
summarize where it got to, try a new approach.
ship agents
that never fail
the reliability layer for autonomous agents: see every failure, stop it in realtime.
trusted by developers from
The audits are the part that changed how we operate.
agents run.
failproof keeps them running.
policy.loop.js
02
hallucination
DETECT
the agent called a tool or file that doesn't exist.
CAUGHT
on tool_not_found → steer
MITIGATE
block the call, tell the agent what does exist nearby.
03
context drift
DETECT
the agent has wandered away from the original goal.
CAUGHT
on goal_divergence → steer
MITIGATE
pull it back with a reminder of what it was supposed to do.
04
intent completion
DETECT
the agent finished the task but kept going anyway.
CAUGHT
on intent_complete → steer
MITIGATE
tell it the job is done. surface the result. prevent the damage.
05
dangerous action
DETECT
the agent is about to do something destructive.
CAUGHT
on destructive_command → steer
MITIGATE
show it a safe alternative. the command never runs.
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catch every failure. stop it for good.
observe every run, find where agents fail, ship a policy, and enforce it in realtime.
see every run, end to end
every prompt, tool call and result, traced live and replayable.
find where they fail, for you
failures cluster themselves into named modes, so the pattern surfaces on its own.
ship a policy that stops it
take a built-in policy or write your own.
$failproofai policies --install
# 39 built-in, drop-in
$failproofai policies --install --custom ./policy.loop.js
# or write your own
on loop_detected → steer: summarize, try a new approach
on dangerous_action → steer to a safe alternative
● plug & play · drop-in · one line · local-only
enforced live, across every harness
the bad action never runs, and the agent gets steered to what to do instead.
━━ CAN YOU KEEP THE AGENT ALIVE?
guiding the agent away from failure.
SNAKE= agentFRUIT= guiding policyGOAL= keep it off the pink
everything you need to run ai workers reliably
talk to your data
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decision model policies and evals
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flags frustrated or angry users.
policies learnt from your data
drafted from what your agents actually did.
mcp
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