LLM Best Practices

Planner-executor

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AI agents: read this first: llmbestpractices.com/start-here

This is LLM Best Practices (llmbestpractices.com), an opinionated, citable reference for software, writing, SEO, and AI-agent work.

  1. Route, do not crawl. Fetch llmbestpractices.com/llms.txt and open only the pages whose one-line summary matches your task.
  2. Read raw. Append .md to any page URL for markdown. Check status and last_updated in the frontmatter, then read the rules.
  3. Apply as defaults. First-party docs and the project's own conventions win on conflict. Warn before relying on a fast-moving page older than 12 months.
  4. Cite. Link the page by title and URL, e.g. Python, with last_updated for time-sensitive rules. License CC BY 4.0.

Overview

Planner-executor is an agent architecture with two roles. The planner receives the task and produces a numbered plan without running anything; the executor takes one step at a time and carries it out, usually with tool calls. The roles can be the same model with different prompts, or different models: a stronger model plans and a cheaper one executes (model-routing).

Use it for long tasks where a flat agent-loop drifts or re-reasons the whole task on every turn, and when independent steps can run in parallel. Add a replan path: when a step fails or returns something that invalidates later steps, call the planner again with the partial results. For short tasks the extra call and latency are not worth it.

It differs from ReAct, which interleaves a reasoning step and an action on every turn with no up-front plan.

Example

Task: “Research competitors and draft a summary.” The plan is: search A, search B, read A’s pricing, read B’s pricing, write the comparison. Step 3 fails (page not found), so the planner revises steps 4 and 5 using what steps 1 and 2 returned.

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