A plan is a hypothesis about the work
Agent planning means turning a user goal into a sequence of inspections, edits and checks. A plan can help when the task spans several files or when a hidden dependency is likely. But the first plan is written before the agent has full evidence. Treat it as a working hypothesis that must change when repository inspection or tests contradict it.
A useful plan is short enough to guide action and specific enough to test. 'Investigate, implement, verify' is too vague by itself. 'Find the route that returns the error, confirm the provider response, patch the mapping and run the auth test' gives observable milestones. Each step should either reveal information or produce a reviewable artifact.
Begin with the acceptance criteria
The agent should understand what success looks like before editing. For a bug, that might be a reproducible failure and a test that passes after the fix. For a feature, it may be a user-visible behavior, an API contract and an accessibility requirement. Constraints such as supported platforms and data handling belong in the plan because they change the solution.
Ambiguity should be made explicit. If the user says improve performance, the agent needs a measurable target or at least a representative workload. It can inspect the current bottleneck while asking for a preference. A plan that silently chooses its own product requirements can deliver polished code for the wrong problem.
Plan from evidence, not file names alone
An agent may guess that a component owns a behavior because its filename is suggestive. Before committing to an edit path, it should trace the actual call or data flow. Read the current implementation, relevant tests and configuration. This initial reconnaissance often changes the plan: the visible component may only render an error produced by a backend or provider.
The plan can record assumptions and confidence. If a search finds two possible routes, compare them against runtime evidence. If there is no test, plan to add or run an alternative check appropriate to the change. Avoid promising a full end-to-end validation when the environment cannot support it.
Use checkpoints that can fail
A strong plan has checkpoints that might disprove the current theory. Reproduce the bug, inspect the expected response, change one boundary and rerun the failing test. If the bug remains, the plan should branch into further investigation. This is different from a checklist where every line can be ticked even if the underlying behavior is still wrong.
For a multi-file change, checkpoints can protect contracts. Update a type, run typechecking, update callers, then run targeted tests. The order should reflect dependencies and feedback speed. Some changes are better as one atomic patch, but the agent can still verify distinct concerns afterward. Planning is a way to manage uncertainty, not to maximize step count.
Adapt when the repository talks back
Test failures, surprising references and user corrections are new evidence. An agent should state what changed in its understanding and revise the next action. Continuing the original plan despite contradictory output is a common failure mode. Equally, endless replanning without a concrete test can become a way to avoid finishing.
The agent should preserve a short decision trail: what hypothesis was rejected, why, and what the new direction is. That helps the user understand a change of course without reading every tool result. Licuse's task history and selected context can support this kind of continuity, but they are aids to current reasoning rather than proof that an earlier assumption remains valid.
Separate reversible work from high-impact action
Reading files, editing a branch and running a local test are usually reversible. Deploying, migrating a live database or publishing a release may not be. A plan should prepare the reviewable patch and validation before any high-impact step. At that boundary, the user needs to see concrete files, expected effects and rollback considerations.
An agent that asks for approval before doing safe preparatory work wastes the opportunity to make the decision informed. An agent that crosses a production boundary without an explicit authorization takes too much control. Planning makes this boundary visible so the workflow can be both efficient and accountable.
The final answer should match the plan's evidence
When work finishes, the agent should explain the changed behavior, name the checks it actually ran and identify material uncertainty. It should not claim a deployment succeeded if only a local build passed. Nor should it present a plan as the deliverable when the user asked for an implemented change. The artifact is the code, document or configuration; the plan is how the agent got there.
A useful planning habit is to ask, after each step, what evidence would make the next step unnecessary or wrong. That question keeps the work anchored in reality. Good agent planning is flexible, observable and finite: it helps a developer reach a tested result without pretending that the initial outline was infallible.
A plan is a revisable path to evidence and a verified change.
Sources & further reading
References support the concepts and product descriptions discussed above. Features, prices and account eligibility can change; check the live documentation. Inclusion here is not a hands-on test or vendor endorsement.