An agent plan needs dependencies, not just a numbered list
Summary
The article proposes that agent plans should explicitly map task dependencies rather than relying on sequential lists, using a podcast workflow example to show how this makes waiting or repeating work more visible and efficient.
Similar Articles
Your agent doesn't need more tools — it needs a job description.
The author argues that AI agents don't need more tools—they need well-defined job descriptions: reusable roles bundling permissions and actions, plus encoded business logic so agents operate by company rules instead of improvising from prompts. Part of a series on making software agent-ready.
Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling
This paper argues that full-horizon planning with lazy replanning is more efficient than step-by-step execution for data-centric LLM agent tasks, using fewer tokens while maintaining accuracy.
Where does your plan live when multiple agents work the same repo?
A developer asks about strategies for coordinating multiple AI agents working on the same codebase, contrasting written plans with mental coordination.
After your agent approves a plan, how often does it actually follow it?
A developer discusses the common problem of AI agents deviating from approved plans during multi-step workflows, seeking advice on how to close the gap between planning and execution.
Are we calling too many workflows “agents”?
The author questions whether many so-called AI agents are better described as workflows, arguing that for repeatable browser tasks, defined workflows may be more reliable than agents that reinterpret steps each time.