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Automation workflows are for work that needs several tasks. A simple task might summarize a folder. A workflow might gather research, inspect a repository, ask several subagents to check different areas, merge their findings, and deliver a final report. Use a workflow when the work has real stages. A single task is easier to operate when there are no dependencies or separate outputs.

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Common shapes

One common workflow is “do this, then use the result”: a research task gathers material and a summary task turns it into a report. Another common shape is “check, then act.” A task inspects a project. If it finds a problem, another task prepares a fix or opens a review thread. For larger jobs, Pioneer can send work to subagents. Each subagent runs in a child thread with its own instructions, model, context policy, and tool policy. The parent task collects the results.

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Review and revision loops

For attached subagent workflows, “collect the results” is an explicit review step. A subagent produces a candidate result. The parent agent reads that candidate and either accepts it or sends revision feedback back to the same subagent. Use revision loops when the parent can cheaply verify the child work: missing files, weak citations, incomplete coverage, wrong output format, unsafe edits, or a result that does not match the original task contract. The feedback should be concrete enough for the child to act on:
The revised work stays attached to the same task run history. Pioneer records the initial candidate, review event, revision turn, and accepted candidate. The parent therefore gets a normal workflow loop without losing the earlier work. Do not use revisions as a vague “try harder” request. If the parent cannot identify the problem, it should ask for a narrower follow-up task or accept the result with an explicit limitation.

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Dependencies

Dependencies tell Pioneer when a task is allowed to run. Use them to keep order clear: run the summary only after research succeeds, skip delivery when the upstream task fails, or wait for several checks before merging results. Retries are useful for temporary failures such as provider timeouts, rate limits, network errors, or MCP server restarts. They should not hide bad prompts or unsafe write behavior.

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Write locks

If several tasks can modify the same files or external target, use write locks. A lock tells Pioneer that only one run should write to that target at a time.
Write locks coordinate Pioneer task runs. They are separate from turn permissions and sandbox/resource policy: use locks for concurrency, and permission modes for access control.

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Delivery

Start with in-app results. Once the workflow is reliable, send results elsewhere only when you need to: another task, a webhook, an artifact store, or an external system. The more places a workflow can deliver data, the more carefully you should test it. Make sure the result does not contain secrets or accidental private context.

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A safe way to build one

Build the workflow manually first. Run each task by itself. Read the timelines. Confirm the provider and model behavior. Check MCP server health. Confirm skills are expected. Then connect the tasks with dependencies. Only schedule the workflow after manual runs are boring.

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Further reading