How Much Should an Agent Plan Before Acting?

Plan to control risk, not to predict the whole run

An agent should plan far enough to expose assumptions, dependencies, review points, and actions that will be expensive to undo. Beyond that boundary, acting and observing usually provides better information than extending an untested sequence.

An up-front plan is a guess

A plan produced before execution describes a world the agent has not observed yet. It may know the request and supplied context, but it does not yet know what a repository search, API response, test run, browser state, permission check, or deployment command will reveal.

The plan therefore contains assumptions even when they are written as confident steps. A file may not exist, an API may return a different shape, a test may expose a second fault, or a tool may lack the required permission. Every surprise reduces the value of later steps whose ordering or content depended on the earlier assumption.

Anthropic’s agent engineering guidance, published 19 December 2024, says agents should obtain ground truth from the environment at each step and use tool or code-execution results to assess progress. It also describes agents as appropriate where the required steps cannot be predicted or hard-coded. That is the limit of a long initial plan: the more open the environment, the more of the plan is speculative.

A useful plan records the intended route and the assumptions that would invalidate it. It should not be treated as a script that remains correct merely because the first step succeeded. Plans are least useful when they make the agent continue past contrary evidence.

The plan is primarily a review artefact

An explicit plan is worth more for review than for execution. Before any work happens, it gives a person something concrete to reject, narrow, reorder, or place behind an approval gate. After an irreversible action, the same objection becomes a recovery task.

For review, the valuable details are the proposed scope, dependencies, mutations, validation points, stopping conditions, and assumptions about the environment. A list such as “inspect, change, test” offers little control because it hides which files, systems, or records may change. The plan needs enough specificity for a reviewer to see where the agent could cross a boundary.

This does not mean every tool call needs advance approval. Read-only discovery and disposable local work can often proceed directly because observation improves the next decision and the result is cheap to discard. Planning overhead is justified where prior objection is materially cheaper than later reversal.

The plan also creates a stable reference for evaluating drift. A reviewer can distinguish a necessary response to new evidence from an unrelated expansion of scope. That matters even when the precise execution sequence changes.

Decomposition pays when work is independent

Decomposition helps most when the resulting subtasks are genuinely independent. Independence means one subtask does not need another’s output, does not mutate shared state that another reads, and does not create an order-sensitive side effect. Only in that case does a wrong ordering cost nothing.

If subtasks share prerequisites or outputs, splitting them early merely moves the dependency into coordination. Workers may inspect different versions of the same state, make incompatible assumptions, or produce changes that require reconciliation. The decomposition has added model calls, context transfer, and synthesis work without removing the underlying sequence.

This is also the condition that should decide whether to use parallel workers. Anthropic’s December 2024 guidance defines parallel sectioning as independent subtasks, while describing dynamic orchestrator-worker decomposition for work whose required subtasks cannot be predicted in advance. The practical boundary is dependency, not the apparent size of the request.

Before delegating work to sub-agents, identify the inputs and outputs of each proposed unit. If changing one unit could alter what another should do, keep the dependency visible and order the work. If each unit can finish against the same fixed inputs and be checked separately, decomposition can reduce elapsed time without making correctness depend on scheduling.

Re-plan on evidence of failure

Re-planning after every step is expensive and converges slowly. Each cycle consumes another model invocation, re-reads some context, and gives the agent an opportunity to reopen decisions that remain valid. On a stable route, repeated planning can oscillate between equivalent formulations without improving the next action.

The March 2025 revision of the autonomous-agent survey separates planning without feedback from planning with environmental, human, or model feedback. Its examples include plans revised after execution errors, object mismatches, unattainable actions, and failed validation. The survey also notes that feedback-aware planning needs more careful design, while initial plans can become non-executable under unpredictable transitions.

Re-planning on failure is usually the better trade. Failure here is broader than a tool returning an error. It includes a violated precondition, an observation that contradicts a planning assumption, a validation result that rejects the output, or a newly discovered branch with materially different consequences. Those events change the route; a successful expected observation usually does not.

The execution loop should therefore preserve the current plan while its assumptions hold, update recorded state after each action, and invoke planning again when evidence invalidates the route. Reliable validation of agent tool results is part of this design because bad or misread feedback can trigger an unnecessary re-plan—or let an invalid plan continue.

Decomposition depth should track reversibility

The depth of planning should follow the cost of undoing a mistake. Cheap, reversible actions need little decomposition because the agent can act, inspect the result, and discard it. Expensive or hard-to-reverse actions need a more explicit route, narrower scope, stronger preconditions, and a defined recovery or approval point.

Reading a file or searching logs usually gathers information without committing the system to a new state. Changing production data, publishing externally, rotating credentials, or executing untrusted code has a wider consequence boundary. The relevant cost is not only whether rollback exists, but how much state, coordination, or lost access must be reconstructed after a bad action.

The Exploring Generative AI series index, accessed 26 August 2026, describes its “To vibe or not to vibe” field report in terms of repeated assessments of error probability, impact, and whether an error will be detected. Reversibility adds the recovery side of that assessment: when detection comes late and restoration is difficult, the agent should plan and seek review before acting.

For a credential change, that may mean resolving affected consumers and rollback access before following the procedure for rotating agent secrets. For code execution, it means establishing the isolation and allowed effects described in sandboxing agent-requested code execution before the first command, rather than trying to contain consequences afterward.

What to determine next

After choosing the planning boundary, define what counts as failure, which observations are trusted, who can approve irreversible actions, and what state must be retained for recovery. Those controls determine whether the agent can safely continue from a short plan or must stop and return a revised one for review.