ClawWatch

A real-time monitoring dashboard for AI agents. As autonomous agents become more capable and more common, the need to observe and manage their behavior across distributed systems becomes critical. ClawWatch fills that gap.

The Problem

AI agents can run autonomously for extended periods, making decisions and taking actions with real-world consequences. Without visibility into what they’re doing, when they’re doing it, and why, debugging failures and ensuring correct behavior is nearly impossible.

Existing monitoring tools were built for traditional services. They don’t understand agent-specific concepts like task queues, tool calls, reasoning traces, or multi-agent coordination.

What It Does

ClawWatch provides a unified dashboard for:

• Agent lifecycle tracking. Spin-up, active, idle, and error states across all running agents
• Task visibility. Real-time view of what each agent is working on, including subtasks and tool invocations
• Reasoning traces. Structured logs of agent decision-making for debugging and auditing
• Alerting. Configurable triggers for stuck agents, error rates, and unexpected behavior patterns
• Multi-agent coordination view. Visualize how agents hand off work and interact with each other

Architecture

The system uses a lightweight SDK that agents include to emit structured telemetry. A central collector aggregates events from all agents and feeds the dashboard in real time.

Agent telemetry flows through a TypeScript event pipeline into a time-series store, with the React frontend polling for updates and rendering the live state view.

Why This Matters

The space is moving fast. Agents that were experimental a year ago are now running in production. Observability for these systems isn’t optional. it’s the foundation that makes everything else trustworthy.

ClawWatch is the tool I wanted when debugging my own agent workflows.