agentic-orchestration

To coordinate multiple AI agents while preserving context, implement structured multi-agent orchestration patterns that enforce modular design, explicit handoffs, and observable execution across hierarchical, parallel, and iterative task workflows.

Effective task decomposition and handoffs in distributed AI rely on pipeline, blackboard, and iterative refinement templates, alongside conductor patterns that ensure explicit state transfer, progress tracking, and structured failure handling.

Set up a top-level orchestrator to decompose the sprint into specialized agents such as Research, Design, and Implementation, then apply hierarchical decomposition to coordinate stages across architecture, development, QA, and docs agents.

Yes, you can run parallel and swarm execution to perform analysis and design simultaneously, then synthesize the combined results using conductor patterns and documented templates for structured state transfer.

Use hierarchical decomposition to break complex tasks into sequential stages with dedicated agents, whereas iterative refinement templates suit workflows requiring repeated analysis, design synthesis, and progressive error recovery cycles.

Error recovery in multi-agent orchestration is enforced through explicit handoff protocols and conductor patterns that provide progress tracking, observable execution, and structured failure handling across specialized distributed AI agents.