Empowering AI Agents with Long-Term Task Planning

The article explains how to implement long-term task planning for AI agents using a Scratchpad and a To-do list tool. These tools allow the agent to break down complex goals into manageable steps and track its own progress autonomously. The author demonstrates the system's power by showing the agent successfully migrating a codebase between two different static site generators.

Key Points

• LLMs are naturally conversational and require explicit frameworks to maintain focus on long-term, complex engineering tasks.
• A Scratchpad tool enables the agent to 'think out loud,' helping it evaluate options and anticipate failure modes before taking action.
• A structured To-do list tool prevents the agent from losing track of progress and ensures only one task is handled at a time.
• The system prompt must define clear protocols for replanning when tools return unexpected results or errors.
• Effective 'done detection' requires the agent to verify structural completion, check against original goals, and perform an uncertainty check.

Sentiment

The overall sentiment is mixed and constructively skeptical. Hacker News generally agrees that planning and task decomposition matter for agents, but it does not converge on the article's specific tool design as clearly necessary. The most thoughtful comments frame scratchpads and to-do lists as useful in some custom harnesses, while many practitioners prefer simpler plan files, human review, and existing agent workflows unless stronger evidence shows the added machinery improves outcomes.

In Agreement

• Custom agents need their own planning and task-tracking affordances when they are not relying on mature coding assistants that already provide those behaviors.
• A strong model can generate useful implementation plans for large codebases, letting humans spend their effort reviewing assumptions and steering architecture instead of drafting every step manually.
• External scratchpads or plan files can preserve precise facts across context compaction, make work resumable after context resets, and reduce dependence on a single model provider.
• Task lists make work more traceable by breaking a broad objective into visible chunks that can be checked, retried, or handed off.
• Agent-generated planning can act like delegated engineering planning: the human still owns judgment, while the agent explores the codebase and proposes concrete implementation paths.

Opposed

• Dedicated planning tools may not justify their complexity because recent models can often follow ordinary text plans, markdown files, and direct file operations well enough.
• Humans should retain responsibility for architecture and decomposition; delegating too much planning can leave users with less understanding of the resulting system.
• Without empirical task-completion data, claims about scratchpads and to-do lists remain anecdotal and hard to compare against simpler prompting workflows.
• Poor programmers or architects may not get better outcomes from AI planning because the tool can amplify weak judgment rather than replace it.
• The term long-term planning is seen by some as overstated compared with more formal planning work in reinforcement learning and related research.