Agent Monitoring

A “walking-dead” AI agent is a process that keeps running, consuming tokens and passing every liveness check, while producing no durable progress toward its task. You detect it not by watching whether the process is alive, but by tracking whether its work actually advances. In one documented case, a SWE-bench Verified agent stayed busy until it used 1.06 million tokens—inspecting, reasoning, and calling tools—yet never wrote the patch, and every health check reported it healthy. This guide shows you how to detect walking-dead states, separate liveness from progress, and patch stalled agents before they burn your budget. ...

Rungraph is an open-source, zero-instrumentation tool that reads the agent session transcripts already on your disk and renders them as interactive graphs. With a single npx rungraph command, it visualizes the orchestrator, subagents, and tool calls of any Claude Code, Codex, Hermes Agent, opencode, or Cursor run — then lets you ask, in plain language through an MCP server, “what happened?” and lights up the exact nodes the answer refers to. ...

MCP observability turns the Model Context Protocol into a two-way interface: AI agents don’t just call tools, they receive ground-truth telemetry from kernel tracepoints, eBPF programs, and kprobes. By exposing low-level system instrumentation through MCP servers, agents get a real-world model of the live system instead of hallucinated state — closing the observability gap that traditional APM leaves wide open. What Is MCP and Why It Needs Observability The Model Context Protocol (MCP) is an open standard that standardizes how AI agents discover and invoke tools, resources, and prompts. Instead of every agent building bespoke integrations with every service, MCP defines a common protocol: a host (the agent runtime) connects to MCP servers, which expose tools the model can call and resources it can read. ...

Multi-agent workflow observability means capturing every delegation hop, tool call, and sub-agent handoff as first-class telemetry instead of relying on flat log lines. Because LLM agents fail silently, teams must trace intermediate reasoning, run offline evals against synthetic datasets, and add regression suites before shipping. This guide explains the observability gap, how to trace delegation hops, and how to build a practical observability and testing stack in 2026. Why Multi-Agent Delegation Demands a New Observability Mindset A multi-agent system distributes a complex goal across specialized agents that hand work to one another. The rationale is straightforward: multi-agent systems solve problems that are difficult or impossible for a single monolithic agent, which is the core justification for delegation patterns in the first place. When an agent delegates a subtask to a colleague agent, a supervisor, or a sub-process, the resulting behavior is emergent, non-deterministic, and often invisible to the humans who wrote the system. ...