What is Planner?

Why It Matters

Execution gets the attention because execution is visible. Planning sets the ceiling. A wrong plan executed perfectly produces a confident, fluent, wrong result, and because every step succeeded, nothing in the run looks like a failure until someone reads the output.

That asymmetry is why planning failures are the expensive kind. A tool error is loud. A bad plan is silent all the way to the deliverable.

How It Works

The planner takes the goal, the constraints, and the tool inventory, and decomposes the work into ordered steps: what happens first, what depends on what, what can run in parallel. In most architectures the planner is the same LLM doing a different job: decomposition rather than generation.

Good planners also replan. When a step’s result disagrees with the plan’s assumption, the plan gets revised rather than followed off the cliff. The plan itself is a first-class artifact: captured in the trajectory, it becomes reviewable evidence of what the agent intended to do.

Where It Breaks

Plan generation is a named failure mode in the agent-failure taxonomy for a reason. Plans get built on stale context, on assumptions about tools that no longer hold, on a reading of the goal that was subtly wrong from the first sentence. And because the planner is usually the same model that executes, the same blind spot produces the plan and approves it.

The other break is rigidity: a plan committed once and never revisited, marching through steps whose premises stopped being true three steps ago.

How Flytebit Handles It

Agent builds at Flytebit treat the plan as an auditable artifact, not a scratchpad: plans captured in the run’s trajectory, replanning triggered on violated assumptions rather than only on hard errors, and eval cases that score plan quality separately from outcome quality. The mechanics are in How AI Agents Work, and the design pass is part of how we build agentic systems.