agent-orchestration

Multi-agent coordination uses swarm, team, and harness patterns to distribute complex AI tasks. Swarm voting handles parallel analysis, while a harness commander manages multi-stage research with structured JSON handoffs for reliable context passing.

The best way to structure context handoffs is using structured JSON exchange combined with RAG-based context retrieval. This enforces reproducible context passing between agents, ensuring reliable multi-agent execution and clear failure logging.

Set up a multi-agent workflow using fixed-role team distribution for role-based tasks like IPO reports. This pattern delegates specialized roles to different agents, applying model tiering and failure-tolerant delegation for robust pipelines.

Yes, you can isolate AI subagents using sandboxed execution environments. This approach enables graceful degradation and clear failure logging, ensuring that isolated subagent failures do not break the entire multi-agent workflow.

Use a swarm pattern for dynamic parallel work and voting analysis, and a harness pattern for multi-stage long-running tasks. Harness patterns excel at deep research pipelines by writing intermediate JSON summaries for stage handoff.

Multi-agent orchestration does not require specific external dependencies to manage model tiering and failure tolerance. It enforces these reliability patterns natively through structured JSON handoffs and failure-tolerant delegation logic.