liveBook · Manning

Planning

As explained in AI Agents in Action

Sequential planning is the organization of dependent actions into an ordered plan in which later actions follow earlier ones.

Planning organizes dependent actions into an ordered sequence that moves an agent from a goal to an outcome. It connects reasoning, tool use, evaluation, and feedback: the agent derives a plan, executes tasks, checks results, and can revise its course. section 10.2 section 11.7

A sequential planner typically follows these steps:

  • Define the goal and select the available actions or tools. section 11.3
  • Ask an LLM to produce a short, structured plan whose subtasks use only those actions. section 11.3
  • Parse the plan and execute its subtasks locally, passing earlier results through the execution context. section 11.3
  • Return results to the LLM for evaluation and summarization, then produce the final user-facing output. section 11.3
  • Add feedback when results require correction, replanning, or adjustment of later tasks. section 1.3 section 11.4

Research and save a Wikipedia summary

Given a goal such as retrieving information about a topic and saving it to a file, the planner can sequence search_wikipedia, get_wikipedia_page, summarization, and save_file. Each later task can use the preceding result; an added summary task can use the assistant’s capabilities even without a dedicated function. section 11.2 section 11.6

Figure 11.7 The planning process for creating and executing a plan
figure

Trace the complete planning loop from constructing a prompt through plan execution and returning results. section 11.3

How AI Agents in Action covers it

How deep it goes
Explains sequential planning through prompts, JSON plans, local execution, context passing, evaluation, and feedback, while deferring detailed planner internals.
Examples
Uses Wikipedia retrieval and file saving, OpenAI Assistants, Nexus planners, and a time-travel reasoning problem.
What it assumes
Assumes basic prompt engineering and reasoning concepts, while introducing planner construction and agent integration.
What you can do after
Apply it via Nexus by building a prompt-based sequential planner that selects tools, executes subtasks, and incorporates feedback.
How it connects
Ties planning to reasoning, evaluation, feedback, prompt engineering, tool use, parallel planning, and autonomous agents.

Also explained in

Compare how 6 books explain planning →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of AI Agents in Action. The section links point to the parts of the book it draws on.