AgentProof Runtime Auditor

Lightweight runtime observability and rollback for long-running AI agents

Problem

Builders of long-horizon agents learn the hard way that the model is only part of the system. Agents lose the original objective, get stuck in tool-call loops, and burn budget; some even resort to PUA-style prompts to coax better behavior, signaling the lack of proper runtime controls.

Solution

A lightweight runtime guard that tracks objective drift per tool call, detects loops and dead ends, triggers retries or pauses, and provides budget guardrails and task rollback points so long agent runs stay on track.

Users

Indie developers and small automation teams using Claude Code, Codex, or similar agentic coding tools

MVP

SDK or local daemon integration; tool-call log collection; repeated-call loop detection with alerts; step budget and timeout interrupts; simple agent health report

Revenue

Per-seat subscription with free tier limited by daily task runs; enterprise adds self-hosting and audit export

Why now

Agentic coding is just entering daily workflows, and long-task reliability is an open problem. Big tools focus on the model; indie devs can fill the runtime gap cheaply.