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I use a team of AI agents by assigning each one a bounded, independent task, giving it the context and success criteria it needs, and keeping one coordinator responsible for integration and review. The key is not to maximize the number of agents; it is to parallelize work that can proceed safely without shared decisions or files.

Start with the outcome, not the agents

Before delegating, define the end state: what should change, what must stay unchanged, and what evidence will show the work is complete. For a code change, that might mean a specific behavior, the relevant tests passing, and no edits outside an agreed area. A task that is small, sequential, or dependent on a decision you have not made is usually better kept in the main workflow.

Each assignment should describe one deliverable, its boundaries, and how the result will be reported. OpenAI’s multi-agent documentation recommends independent tasks with clear questions and expected results. OpenAI’s multi-agent guidance gives examples such as reviewing separate documents or investigating different possible causes of a failure.

Choose work that can actually run in parallel

Parallel work helps when agents can make progress without waiting on one another. It becomes less useful when one task needs another agent’s output, several agents are changing the same interface, or a fixed sequence matters. OpenAI’s guidance notes that parallel delegation can increase token use and may be a poor fit for dependent tasks or shared mutable resources. Read the Responses multi-agent guide for the trade-offs.

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Situation How to organize it
Independent research, analysis, or implementation areas Assign separate deliverables and let agents work concurrently.
A task depends on a migration, interface, or design decision Finish or settle the prerequisite first, then start the dependent task.
Multiple agents may edit the same files or interface Assign one owner, divide the files explicitly, or coordinate edits before work begins.
The sequence must be predictable Use code-driven orchestration for explicit ordering; reserve model-directed decisions for cases that benefit from flexible planning.

OpenAI’s account of Symphony describes a dependency-linked task graph in which agents start on unblocked work. It is one orchestration design, not a universal prescription: the authors describe Symphony as a reference implementation rather than a standalone product. See the Symphony account.

Give each agent useful, stable repository context

Agents need enough shared context to follow the project’s conventions without being buried in unrelated details. Put durable guidance—such as repository structure, coding conventions, relevant commands, and constraints—in the instruction-file format supported by your coding-agent setup. In each task, add the local context: the files or components in scope, assumptions, dependencies, and expected result.

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Do not treat instructions as a one-time setup. The VS Code guide recommends starting from a recurring problem you have observed, recording a baseline, making the smallest useful customization, and checking whether it applies as intended. VS Code’s agent customization guide explains that approach.

Keep one integrator accountable for the result

A coordinator—whether a person or a designated agent—should own the combined change. That role includes checking what each worker delivered, resolving conflicts, ensuring interfaces fit together, and verifying the result against the original success criteria. A collection of individually plausible outputs is not a finished change until it works as a whole.

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  1. Review each handoff. Ask for a concise summary of changes, files touched, assumptions, and checks performed.
  2. Integrate deliberately. Compare changes for overlap or incompatible decisions before combining them.
  3. Verify the combined result. Run the relevant project checks and inspect the final diff against the stated scope and success criteria.
  4. Retain human approval. Treat agent output as a proposal to review, especially before repository changes are accepted or shipped.

The OpenAI Agents SDK describes code-driven orchestration as useful when predictable sequencing, cost, or performance matters, and model-directed orchestration as useful when planning needs flexibility; the approaches can be combined. See the Agents SDK orchestration guide.

Set permissions and review boundaries

Decide what agents can read and change before work starts. GitHub says Agentic Workflows use declared permissions and safe outputs, with repository permissions read-only by default and writes restricted to validated outputs. Its documentation also says to “Keep human review in the loop.” Read GitHub’s Agentic Workflows documentation.

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That workflow supports GitHub Copilot, Anthropic Claude, OpenAI Codex, and Google Gemini, with authentication specific to each engine. This is an example of available tooling, not a comparative ranking of those agents.

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Choose the setup by its trade-offs

There is no evidence-backed universal best number of agents or fixed role chart. Decide based on the shape of the work and the cost of coordinating it:

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  • Task independence: Can each worker make progress without waiting for another?
  • File overlap: Are multiple agents likely to edit the same files or interfaces?
  • Context isolation: Would focused, separate contexts help agents stay on task?
  • Coordination burden: Can you review, reconcile, and integrate the outputs?
  • Execution control: Does the work need a deterministic sequence, or is flexible planning acceptable?
  • Permissions and review: What can agents read or change, and who approves the result?

OpenAI Codex is described as usable across ChatGPT, an editor, and a terminal. See the Codex product page. The right surface depends on your workflow; the available examples do not establish which setup is best for every team.

A practical delegation pattern

  1. Write the acceptance criteria. State the desired behavior, constraints, and checks that count as done.
  2. Map dependencies. Identify tasks that can start now and those that must wait for a prerequisite.
  3. Assign bounded tasks. Give each agent one clear question or deliverable, with its context and expected handoff.
  4. Coordinate shared areas. Set an owner or agree on boundaries before agents touch overlapping files or interfaces.
  5. Integrate and verify. Have one owner reconcile the work, run relevant checks, and review the final change.

Use the smallest amount of parallelism that removes real waiting. More agents are not automatically more productive: additional coordination and token use can outweigh the benefit when work is coupled or the execution path is already clear.

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