Foundations
MDX
Planning and Reasoning in AI Agents
When agents need explicit plans, how plans change, and why execution feedback matters more than elegant reasoning.
Editorial review: clarity, operational relevance, safety boundaries, and source quality.
Plans reduce ambiguity
For multi-step work, a short plan gives the agent a sequence of verifiable milestones. It also gives users and developers a visible structure for reviewing progress.
Planning is most useful when actions have dependencies, meaningful cost, or different permissions. A plan should name observable milestones rather than internal thoughts: identify records, gather evidence, prepare a change, obtain approval, verify the result. Short tasks often need no explicit plan at all.
Plans should stay editable
Tool results can invalidate an assumption or reveal a shorter path. Strong agents revise plans when evidence changes rather than following an outdated checklist mechanically.
Treat the plan as state, not a promise. Mark steps complete only from tool evidence, record why a step changed, and replan when an assumption fails. Replanning should also consume a budget; otherwise an agent can repeatedly rewrite an elegant plan without making progress.
Reasoning needs grounding
A persuasive explanation is not proof that a task succeeded. Reliable agents connect claims to observations such as test output, retrieved records, or explicit confirmations.
Verification should be structurally independent of generation. Run tests after code changes, reread the record after an update, or compare claims with cited passages. Asking the same model whether its own answer is correct can catch obvious slips, but it does not provide external evidence of success.
Practical example
Planning a data migration
The agent first inventories schemas and constraints, then produces a dry-run transformation and validation report. A person approves the mapped fields before any write. After execution, the agent compares source and destination counts and samples exceptions. If the dry run exposes an undocumented enum, it adds a mapping task and returns to validation instead of following the original plan blindly.
Field checklist
Apply it in practice
- Use plans only when dependencies justify them.
- Phrase steps as verifiable outcomes.
- Revise plans when observations invalidate assumptions.
- Verify completion through tools or external state.
Decision framework
Questions to answer before you build
Plans are valuable when they expose dependencies and verifiable milestones. They become harmful when the agent treats them as fixed commitments despite contradictory evidence.
Does this task need an explicit plan?
Plan when actions have dependencies, cost, or different permissions. A simple read-answer task may need only a direct action and verification.
Can each step be verified?
Phrase milestones as observable outcomes—records identified, tests passing, approval obtained—not internal activities such as think or analyze.
When should the plan change?
Replan when a prerequisite fails, new evidence changes scope, or a shorter safe route appears. Record why the revision occurred.
Common failure signals
Watch for these warning signs
- Producing elaborate plans that consume time without reducing uncertainty.
- Marking steps complete from model narration rather than tool evidence.
- Asking the same model to verify its own unsupported conclusion.
Selected primary references
Continue with the source material
These sources inform the wider editorial perspective for this topic. They are not presented as line-by-line citations for every statement.