Agent orchestration

Use GPT-6 Astra async tool calling for independent work while keeping pending results, dependencies, and final answers consistent.

Trace assignments, inputs, tool results, and accepted artifacts to find where a multi-agent workflow first went off course.

Compare Astra and Fable 5.1 on your actual workflow using accepted results, review effort, latency, and the full cost of completion.

Choose between a coordinator that combines specialist results and a handoff that lets a specialist take over the conversation.

Run a bounded pilot and compare accepted output, review time, cost, and failure recovery before expanding the agent team.

Review forced tool selection, structured results, and fallback behavior before switching an existing Claude workflow to Fable 5.1.

Map dependencies before running agents at once, and keep parallel work from creating conflicting edits or premature conclusions.

Build a small handoff packet with the goal, current artifacts, decisions, evidence, and unresolved questions.

Separate safe retries from uncertain external actions, and preserve completed work when a multi-agent step fails.

Create clear escalation rules so routine tasks stay simple and difficult decisions reach a worker equipped to handle them.

Assign distinct research questions and require evidence for each claim before a coordinator combines the findings.

Budget for the whole task, including worker calls, retries, tools, and the time needed to review the result.

Use clear file ownership, separate workspaces, and one integration step when several coding agents work on the same project.

Keep a shared task record and explicit handoffs when OpenAI and Claude agents work on the same project.

Give Claude Fable 5.1 a focused review assignment with requirements, evidence, and a clear distinction between defects and preferences.

Give GPT-6 Astra clear delegation rules, concrete completion criteria, and a manageable view of the work it coordinates.

Follow a practical example of a coordinator assigning work, checking results, and deciding when a multi-agent task is finished.

Resolve conflicting agent findings through requirements and evidence rather than repeated debate or majority voting.

Decide whether a task needs multiple agents by looking at dependencies, context, and the cost of combining their work.

Specify the task, inputs, file ownership, completion evidence, and escalation rules before assigning work to an AI subagent.