AgentWatch
Two-line proxy that kills runaway agent loops with runtime budget enforcement and anomaly detection
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agent-watch.devAbout AgentWatch
AgentWatch is a budget-enforcement and anomaly-detection proxy for LLM agents, built around the failure mode where an agent enters a loop and burns hundreds of dollars before anyone notices. The distinction it draws is between detecting that a budget was crossed — which every billing dashboard does, after the money is gone — and detecting that an agent is spiralling, which is a behavioural signal available earlier. Integration is deliberately trivial and requires no SDK: you change the base URL on your existing OpenAI client to AgentWatch's proxy endpoint and combine your AgentWatch key with your provider key, two lines of change in Python, TypeScript or cURL. Requests still bill to your own provider account; AgentWatch sits in front and returns a 402 when a budget ceiling is hit, with the overage stated in the response. Beyond the hard ceiling there is behavioural anomaly detection for runaway loops, agent replay for reconstructing what a run actually did, and cross-provider forensics for tracing spend across more than one model vendor. Paid tiers add edge prompt caching, custom anomaly rules, team and per-agent budgets and Slack webhook alerts, while the enterprise tier covers SLA monitoring, shadow-AI discovery, SOC 2 exports, data residency, SSO and Azure OpenAI plus AWS Bedrock support. The control plane is open source on GitHub and latency at the edge is single-digit milliseconds.
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Key Features
AgentWatch Pros & Cons
✅ Pros
- +Two-line integration with no SDK is about as low-friction as instrumentation gets
- +Model spend stays on your own provider account rather than being resold
- +50,000 free requests/month is a genuinely usable free tier
⚠️ Cons
- −Adds a network hop in front of every model call
- −Request-count tiers do not track cost — cheap and expensive calls count the same
- −Alerts and team budgets require the $39/mo tier
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