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A task in an agent plan is the right size when it produces one outcome you can check, and it is small enough that the agent knows what to do first, what it depends on, and how to tell when it is finished. The official guidance covered here does not set a fixed number of steps, minutes, or tokens. Split a task when it bundles separate actions or hides a decision. Keep work together when its parts share context and can only be verified as one unit.
What every plan item needs before work starts
Before an agent can act on a plan item, the item should answer four questions. If any answer is missing, the agent has to invent it, and that is where plans drift.
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- Outcome: the one result this item is responsible for producing.
- Action: the step or tool use that advances that outcome.
- Prerequisites: the earlier results, files, permissions, or decisions the item depends on.
- Completion signal: the observable result that means the item is done.
For software work, the Visual Studio Code guidance on planning with agents (page dated 30 September 2026) makes the grounding point directly: an actual plan should name the files and code patterns in your project rather than list generic tasks. A plan item that says “add validation to the import endpoint” is far easier to execute and check than “improve input handling.”
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The sources agree on a functional boundary rather than a numeric one. Snowflake’s article on AI agent planning describes planning as breaking a goal into dependent subtasks, selecting actions or tools for each, tracking intermediate state, and revising as observations come in. GOV.UK’s description of agentic workflows treats task decomposition as dynamic and iterative, with monitoring feeding back into the plan. Neither frames a particular task length as the correct one.
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The most specific guidance on instruction units comes from Microsoft Learn’s “Write effective instructions for declarative agents.” That page is written for instructions given to declarative agents, so its unit is an instruction step in that setting. Applying the same reasoning to a general agent plan is a reasonable extension, but it is an extension, not a rule Microsoft states for plans.
When to split a task
When it contains several independently meaningful actions
Microsoft’s guidance advises breaking multi-action instructions into separate units: “Break multiaction instructions into clearly separated units. This approach reduces ambiguity and prevents the model from merging or reinterpreting tasks.” A typical case is a single item that says “extract the metrics and summarize the findings.” The summary depends on the extraction, but if both happen in one step, nobody can tell whether a partial extraction counts as progress. Two items with a clear transition between them fix that.
When it hides a choice that must be made first
Some tasks look like one action but contain a decision. “Change the export format” hides a choice between CSV and XLSX, and possibly a question about which columns survive the change. Pull the decision into its own item, state the criterion for making it, and let the implementation item depend on the decision’s result. Otherwise the agent makes the choice silently inside the build step.
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When its result cannot be checked until unrelated work is also finished
If an item only shows whether it worked after a second, unrelated change lands, the two should be separated. Each item should have its own checkpoint so a failure points to one cause.
When to keep work together
Splitting is not automatically safer. A task should stay whole when its parts share the same context, depend tightly on each other, and produce one result that a reviewer would judge as a unit. Splitting such work forces the agent to repeat setup, reload the same files, and reconcile pieces that were never meant to be separate. Breaking a task into single-line boxes to look granular usually adds coordination cost without adding control.
Choosing the boundary: a comparison
When you are deciding whether a candidate item should be split or combined, these factors point in different directions.
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| Factor | Favors splitting | Favors keeping together |
|---|---|---|
| Outcome clarity | The item would produce two or more results that a reviewer would judge separately | The item produces one understandable result |
| Actionability | The agent would have to choose an approach or goal that was never stated | The approach is already determined by the project and the instruction |
| Dependency structure | Parallel actions could be confused, or one part must finish before another starts | The parts depend on each other so closely that order is obvious |
| Verifiability | Completion cannot be checked without unrelated work | One check covers the whole outcome |
| Context reuse | Each part can be set up independently with little shared state | Splitting would force repeated setup or lose shared context |
| Replanning cost | The work is uncertain, and a wrong assumption would be expensive to undo | The work is routine, and a checkpoint would add delay without reducing risk |
| Coordination overhead | Extra items are cheap to track and clarify the next step | Extra items add model calls or review burden without improving control |
These factors are a practical synthesis of the sources. None of them is a published scoring system, and none gives a numeric threshold.
When a hierarchy helps and when it costs more
Google Cloud’s Architecture Center describes hierarchical task decomposition for ambiguous, open-ended problems, where a top-level agent delegates research, planning, and synthesis to lower-level work. That structure is useful when the problem is genuinely open. It also has costs: more model calls, higher latency, greater operational expense, and more architecture to maintain. For a task with a clear outcome and a known approach, a flat list of well-bounded items is usually the better choice.
Writing a completion check
Each item needs a check that matches its outcome. In a coding plan, the checks depend on the requested change, but they commonly include:
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- Tests for valid inputs and for invalid inputs.
- Behavior when results are empty.
- Compatibility with existing behavior that the change should not disturb.
- A manual check where automated tests cannot show the requested behavior.
Snowflake’s article, quoting Arun Agarwal, Principal Product Marketing Manager, AI/ML, suggests asking whether the agent “used the right tools in the right order.” That question works well as a review point for a plan, because it tests sequencing, not just the final output.
Here is an illustrative example. A vague item such as “add CSV export, update the docs, and fix the tests” bundles three outcomes. A better split is three items: implement the export and return a file for a sample report; update the export section of the documentation and confirm the described steps match the new behavior; and run the existing report tests and record any failures tied to the export change. Each item has a result a reviewer can inspect on its own.
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A plan is a working hypothesis, not a contract. When an intermediate result shows a new dependency, a failed assumption, or a changed condition, the remaining items should be revised. GOV.UK’s description of agentic workflows and Snowflake’s account of inspecting results both describe this loop.
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Longer sessions need more checkpoints. Snowflake’s article cites a May 2026 LongDS-Bench result, in which the best-performing model reached 48.45% average accuracy and performance fell by nearly 47 points from early to late turns. That benchmark concerns multi-turn performance, and it does not establish a task-size threshold. It does suggest that a long, uninterrupted run of items is a weak basis for trust, so placing verification checkpoints at natural breaks is worth the small added cost.
Practical rules to take away
- Size each item by its outcome, actions, dependencies, and completion signal, not by a step count.
- Split multi-action items and hidden decisions, and make the order of dependent items explicit.
- Keep tightly related work together when it shares context and is verified as one result.
- Add hierarchy only where the problem is ambiguous enough to justify its cost.
- Revise the plan when intermediate results change what should happen next.
The Microsoft Learn guidance is written for declarative agent instructions, and the Snowflake figure comes from a vendor article citing a benchmark. Treat both as supporting evidence for the principles above rather than as universal standards for every agent system.
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