Failproof AI

This is an end-to-end failure layer for teams building and deploying AI agents in production. It watches agent runs, identifies where workflows go off track, and applies policies designed to prevent the same classes of failures from happening again.

The core workflow connects observation, failure identification, policy creation, and real-time enforcement. Instead of treating agent failures as incidents that are only reviewed after the fact, the platform turns repeated failure patterns into enforceable rules that can intervene before a problematic action completes.

Key Features

Live Agent Tracing

Record the details of agent runs so teams can understand exactly what happened.

• Capture prompts
• Record tool calls
• Capture tool results
• Replay runs step by step
• Trace agent behavior across a workflow

This gives engineers a detailed view of the sequence leading up to a failure rather than relying on a final error or outcome.

Failure Clustering

Group recurring agent problems into recognizable failure modes.

Examples include:

• Loops
• Hallucinated tool calls
• Context drift
• Intent completion issues
• Dangerous actions

By clustering repeated failures, teams can identify patterns rather than treating every problematic run as an isolated incident.

Policy Layer

Turn identified failure patterns into rules that can govern future agent behavior.

The platform supports:

• Built-in policies
• Custom policies
• Custom rule files
• Policy-based agent controls

This creates a direct connection between what teams learn from previous failures and how future runs are handled.

Realtime Enforcement

Apply policies while an agent is running rather than waiting until after the action has already happened.

Depending on the configured policy, the system can:

• Block an action
• Warn about an action
• Stop the run
• Ask a human for approval before proceeding

This provides a control layer between an agent's intended action and execution.

Broad Agent Harness Support

The platform is designed to work across different AI coding and agent environments.

Supported setups listed by the source include:

• Claude Code
• Codex
• Cursor
• Copilot
• Gemini CLI
• LangGraph
• Other agent setups

This makes the failure layer applicable across different agent stacks rather than tying monitoring and enforcement to a single framework.

Local-Only Setup

The platform is described as a plug-and-play, local-only setup.

Its CLI-centered workflow is suited to teams that want to run the failure and policy layer directly within their own development environment.

Built For Production AI Agent Teams

The platform is aimed at teams moving beyond AI agent demos and experimenting with autonomous systems that need to operate reliably across repeated production runs.

Founders can use it to introduce guardrails as autonomous workflows become more capable and consequential.

Engineers can trace failures, identify recurring patterns, and turn those patterns into enforceable policies.

Researchers can study agent behavior across runs and classify failure modes rather than examining individual failures in isolation.

Common Use Cases

Agent failure monitoring: Trace production runs and understand where an autonomous workflow went off track.

Failure pattern detection: Cluster recurring problems such as loops, context drift, or hallucinated tool calls.

Agent guardrails: Apply built-in or custom policies to control risky behavior.

Dangerous action prevention: Stop, warn, or require human approval before an agent performs a configured action.

Coding agent control: Add a failure and policy layer to environments such as Claude Code, Codex, Cursor, Copilot, or Gemini CLI.

Production agent reliability: Move from reactive debugging toward continuously enforced controls for repeated failure classes.

Why It Matters

Monitoring alone can tell a team that an AI agent failed, but it does not necessarily prevent the same failure from happening on the next run.

This platform closes that gap by connecting live tracing → failure clustering → policy creation → realtime enforcement. Once a recurring failure is identified, teams can turn it into a rule that can block, stop, warn, or request human approval before the same type of action causes another problem.

That makes the system particularly relevant for autonomous agents operating beyond controlled demos, where repeated failures can become operational or safety concerns.

Control AI Agent Failures Before They Repeat

Trace every agent run, identify recurring failure modes, and turn those findings into enforceable policies with a local, CLI-centered failure layer built for production AI agent workflows.