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Planner-executor is an agent design pattern that separates task decomposition from tool-mediated actions.
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It can make state transitions and retries easier to inspect, but separation alone does not make a system reliable.
In a planner-executor design, a planner converts a high-level goal into steps, while an executor carries out a step through tools, APIs, or another model. The executor returns observations or errors to shared state; the planner can then continue, revise the plan, or stop. Some implementations use one model for both roles, and some use different models or deterministic tools. A plan is not proof of progress: systems need explicit constraints, success criteria, failure handling, and a bounded number of tool calls. These are implementation choices rather than properties of every planner-executor agent.
A coding agent drafts a sequence of edits and tests, then checks the test results before updating the plan.
A data workflow plans a sequence of transformations and validates each produced output.
A research assistant splits a comparison question into bounded searches and records evidence returned by each tool.
A support workflow checks the resulting account state before telling a user that an action completed.
Planner-executor designs are one of several agent architectures. Evaluate them on task completion, tool errors, cost, latency, and recovery from changed or failed steps rather than assuming the role split improves performance.
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Planner-executor is an agent design pattern that separates task decomposition from tool-mediated actions. It can make state transitions and retries easier to inspect, but separation alone does not make a system reliable.
Separating decomposition from actions can make a multi-step workflow easier to inspect and organize. Reliability still depends on the planner, tools, error handling, and verification.
A plan can be revised when the implementation includes feedback from execution; that may help respond to errors or new information, but it depends on the agent design.
An explicit plan can help track intended steps in a longer task, but it does not prevent drift or hallucinated progress by itself. Check tool observations and the final state.
An executor performs its assigned step through tools, APIs, or another model, then returns observations or errors to the workflow.
Los resultados intermedios y los pasos completados se retroalimentan para que el planificador pueda decidir qué hacer a continuación.
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