How Agents Plan Tasks with To-Do Lists

What’s Happening

Breaking it down: Understanding the process behind agentic planning and task management in LangChain The post How Agents Plan Tasks with To-Do Lists appeared first on Towards Data Science.

Planning is something we all do naturally and regularly. In our personal lives, we often keep to-do lists to organise holidays, errands, and everything in between. (it feels like chaos)

At work, we rely on task trackers and project plans to keep teams aligned.

The Details

For developers, it is also common to leave TODO comments in the code as reminders for future changes. Unsurprisingly, LLM agents also benefit from clear to-do lists to guide their planning.

To-do lists help agents plan and track complex tasks more effectively, making them especially useful for multi-tool coordination and long-running operations where progress needs to be visible. Coding agents like OpenAI Codex , Cline , and Claude Code (which I use regularly) are prime examples of this concept.

Why This Matters

They break complex requests into an initial set of steps, organize them as a to-do list with dependencies, and update the plan in real time as tasks are completed or new information arises. Example of a plan generated agent (Claude-4. 5-sonnet) on the right-hand pane of Cursor | Image by author This clarity enables agents to handle long sequences of actions, coordinate across different tools, and track progress in an understandable manner.

The AI space continues to evolve at a wild pace, with developments like this becoming more common.

Key Takeaways

• (1) Example Scenario for Planning Agent Let’s look at the example scenario to anchor our walkthrough.
• We will set up a single agent to plan a travel itinerary and execute the booking tasks.

The Bottom Line

(1) Example Scenario for Planning Agent Let’s look at the example scenario to anchor our walkthrough. We will set up a single agent to plan a travel itinerary and execute the booking tasks.

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