Your AI agent is silently spinning — calling tools over and over, retrying with corrupted context, burning tokens, and nobody catching it. We audit your agent's prompts, tool definitions, and success conditions, then deploy loop-detection guardrails that alert you the moment an agent starts looping.
See Plans & Pricing Free Checklist →Most AI agent failures in production are silent. The agent gets stuck in an infinite loop — calling a tool, getting an error, retrying with corrupted context, calling again. No crash, no error log, just mounting API costs and degraded responses. A 2026 Connections survey found this is the #1 pain point in production agent deployment. Standard observability tools miss it because the agent appears "running."
We audit your agent's architecture for loop vulnerabilities: ambiguous success conditions, missing max-iteration limits, tool failure cascades, and context window poisoning. Then we deploy guardrails that detect, halt, and alert on loop conditions. Circuit breakers, dead-letter queues, agent health scorecards, and Grafana dashboards. Your agents stop burning money silently.
4 common failure modes — all silent, all costly
Tool fails → agent retries → fails differently → context grows → agent gets confused → retries forever. No circuit breaker.
Agent keeps appending to conversation context without trimming. Context hits limit, agent loses coherence, starts repeating itself.
Agent doesn't know when "done" means. It generates output, re-checks, tweaks slightly, generates again. Never self-terminates.
Tool A returns unexpected data → Tool B crashes → agent tries Tool C as fallback → Tool C makes things worse → agent tries Tool A again.
From audit to production guardrails
We review your agent prompts, tool definitions, success/failure conditions, and retry logic. Identify every loop vulnerability.
Max-iteration limits, tool-failure circuit breakers, success-condition detection, context window monitors, and dead-letter queues.
Grafana dashboard showing: loop events, agent health score, tool failure rates, context window usage, retry patterns. Real-time alerts.
Daily/weekly score: loop-free uptime, tool success rate, average iterations per task, max context utilization. Know your agent's health.
Telegram, Slack, email, webhook — choose where alerts fire when a loop is detected. Never find out from a customer complaint.
Documented patterns: how to write loop-resistant prompts, define clear success conditions, and design fault-tolerant tool chains.
From quick scan to full guardrail deployment
Foot in the door — worth your email
Covers: success condition clarity, tool failure isolation, max-iteration limits, circuit breaker patterns, loop detection telemetry, dead-letter queues, ambiguous instruction audit, context window overflow detection, retry budget caps, and agent health scorecard. Every developer running production agents needs this.
Download Free →Classic signs: API costs spike without traffic increase, responses get slower over time, agents repeat similar tool calls, users complain about weird or incomplete answers. But the only way to know for sure is loop detection telemetry — which is what we build.
Yes. We've deployed guardrails for LangChain, LangGraph, Claude Code, Codex, Cursor, Hermes Agent, and custom agent frameworks. The principles are framework-agnostic.
No. Guardrails are lightweight checks injected at key decision points (tool calls, iteration boundaries, context updates). Typical overhead: <5ms per check.
Guardrails have configurable thresholds and grace periods. False positives are rare and we tune them during the 30-day adjustment window. The dead-letter queue preserves the failing request for inspection.
Yes — and that's the most common use case. We deploy guardrails as middleware that wraps your existing agent without changing its core logic. Zero downtime deployment.
Standard observability (LangSmith, LangFuse, etc.) shows you what the agent did. It doesn't tell you if the agent is stuck in a loop. Loop detection is specialized logic that recognizes loop patterns — repeated identical tool calls with no progress, context growth without output, retries with corrupted state. This is not a general-purpose monitoring tool.