{
  "id": 19372084,
  "title": "6 Planning Patterns for AI Agents and When Each One Fits",
  "url": "https://urgent.news/2026/11/10/6-planning-patterns-for-ai-agents-and-when-each-one-fits",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-10T18:00:00.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/shaheryaryousaf/6-planning-patterns-for-ai-agents-and-when-each-one-fits-3ac3"
  },
  "original_language": "en",
  "account": "When building AI agents for tasks like planning and executing plans, it is crucial to choose the right reasoning pattern for the specific job at hand. Rather than blindly adopting a popular pattern, the first step should be to analyze the task itself. Ask key questions like whether the steps are predictable, whether the agent needs to adapt based on new information, and what success criteria look like. Only after understanding the core requirements should you consider which planning strategy to employ.\n\nThere are five main planning patterns to evaluate:\n\nFixed Workflow: This is a straightforward approach where a developer specifies a predetermined sequence of steps. It works well for repeatable tasks with a clearly defined process, such as generating a weekly report. The agent knows exactly what to do and can handle each step individually, validating intermediate results before moving on. However, a fixed workflow may be less flexible if the task requirements change.\n\nPlan Then Execute: In this pattern, the AI model first proposes a sequence of steps to achieve the goal, which can then be executed by the system or another model. This is useful when the task may vary slightly each time, but still has a clear decomposition into discrete actions. For example, a comparison of software options could be handled by the model generating a plan to gather candidates, retrieve pricing, compare features, and produce a recommendation. The application can then validate each step, request clarifications, or halt execution if something looks amiss. Crucially, the model's plan is not trusted blindly - each action must be checked against an allowlist of accepted sources or tools.\n\nReAct-Style Tool Use: This is a more dynamic loop where the agent alternates between reasoning about the next action and actually carrying it out by calling a tool (such as a search API). The agent observes the result of each tool call and decides on the appropriate next step. This allows the agent to adapt on the fly, for instance if a product's pricing has changed. However, it can also make the process harder to predict, leading to unnecessary tool calls or excessive iteration. To avoid this, set strict limits on the number of iterations, types of tools used, runtime, and cost. Also log all tool requests and responses, so failures can be traced back.\n\nRouting to Specialists: When a task is complex, the agent can delegate certain subtasks to specialized agents or models. For example, pricing comparison could be split into individual lookups for each software option. This can enable more accurate and efficient information gathering. The routing decision itself may be part of the overall plan.\n\nParallel Work: In some cases, the agent can perform independent subtasks simultaneously. For instance, while one component looks up pricing information for each software option, another component could draft a recommendation based on partial results. The final synthesis merges these parallel results. This can speed up execution, but introduces complexity in reconciling conflicting outputs.\n\nDraft-Evaluate-Revise: This iterative pattern is good for creative tasks that require multiple passes, like writing a report. The agent first drafts an initial version, then evaluates it against some criteria (e.g. completeness, style, factual accuracy). Based on the evaluation, it revises the draft. This loop continues until the agent is satisfied with the result. The key is to define clear stopping conditions, such as a maximum number of revisions or a time limit.\n\nNo matter which pattern is chosen, it is vital to validate all actions and outputs. Plans are just proposals - the world is not guaranteed to cooperate with the agent's assumptions. Be sure to have checks in place to verify pricing dates, confirm tool outputs, and ensure the final recommendation meets the user's needs. With the right planning pattern and proper safeguards, AI agents can reliably turn high-level goals into concrete actions.",
  "summary": "TL;DR for devs Start from the task, not from a popular reasoning pattern. Use a fixed workflow for predictable steps; consider model-generated plans when tasks vary. Other patterns: plan then execute, ReAct-style tool use, routing to specialists, parallel work, and draft-evaluate-revise. A plan is a proposal, not a guarantee, so validate actions and results. Set stopping conditions (max steps,…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}