tai/research/cap-protocol/openai-agents-python-multi-agent-orchestration.md

OpenAI Agents SDK: Orchestrating multiple agents

Metadata

Field	Value
Institute	Molodetz Institute — Research & Retrieval Division
Subject	cap-protocol
Document type	Source Dossier
Tier	1
Source title	OpenAI Agents SDK: Orchestrating multiple agents
Source URL	https://openai.github.io/openai-agents-python/multi_agent/
Source publisher / author	OpenAI
Source type	vendor docs
Source date (published)	Unknown — not stated
Date researched	2026-10-25
Researched by	tai (agent), model: Claude Sonnet 5, on behalf of retoor
Research method	direct page fetch
Confidence	High
Status	Active

Summary

Short orchestration guide separating LLM-driven from code-driven orchestration and agents-as-tools from handoffs.

Key findings

• LLM-driven orchestration lets the model plan and choose steps; code-driven orchestration puts application code in charge of which agents run in what order, making speed, cost and performance more predictable.
• Agents as tools: use when a specialist handles a limited subtask 'but should not take over the user-facing conversation'; the manager keeps control of the reply.
• Handoffs: use when 'routing itself is part of the workflow'; the specialist owns the rest of the turn.
• Best practices: invest in good prompts, monitor and iterate, let the agent introspect and improve, build specialists that each excel at one task, use evals.

Pros / strengths

Names the decision rule (who owns the reply) in two lines.

Cons / limitations / caveats

Vendor guidance; no data. Overlaps with Anthropic's orchestrator-workers description.

Relevance to tai

tai's @agent prompt is a handoff-like human-chosen route; a model-callable ask_agent would be the agent-as-tool form. This page backs choosing the tool form when tai must stay in control of the answer.

Changelog

Date	Change
2026-10-25	Initial dossier created.