multi-agent-coordination

Trace multi-agent handoffs by attaching standardized span attributes like agent.name, agent.parent_id, and handoff_reason to instrumentation hooks, revealing supervisor-worker relationships and decision paths across complex AI runs.

Multi-agent coordination tracing works with LangGraph, CrewAI, and AutoGen patterns, supporting context propagation across hub-and-spoke, pipeline, parallel, and hierarchical orchestration models to capture composite agent decisions.

Multi-agent observability requires standardized span attributes including agent.name, agent.parent_id, workflow.state, and handoff_reason, which enable end-to-end tracing of supervisor-worker delegation and task handoffs.

Coordinate multi-agent workflows by modeling parent-child relationships and attaching instrumentation hooks that capture handoff_reason and workflow.state, allowing you to verify task delegation across hierarchical agent structures.

Multi-agent tracing loses context during handoffs when instrumentation hooks fail to propagate parent-child relationships or omit agent.parent_id attributes, breaking the supervisor-worker hierarchy chain across orchestration patterns.

Model parallel and hierarchical agent orchestration by mapping parent-child spans with agent.name and workflow.state attributes, capturing handoff_reason to achieve end-to-end observability across complex multi-agent ecosystems.