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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 four things: what the outcome is, which action moves it forward, what must already be true before it starts, and what observable result shows it is finished. None of the official guidance behind this article sets a universal number of steps, minutes, or tokens. The boundary is functional, not numeric.
The four tests a task must pass
Before deciding whether a task is too big or too small, check whether it is ready to execute at all. A plan item is ready when it answers these four questions:
- Outcome: What single result is this task responsible for producing?
- Action: What action or tool use advances that result, without the agent having to invent a goal or pick an approach nobody stated?
- Prerequisites: Which earlier results, inputs, or decisions must exist first?
- Completion signal: What observable result shows the task is done?
If any one of the four is missing, the task needs work, regardless of how long or short it is. A ten-word item can fail these tests, and a longer item that passes all four is usually well sized.
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When a task should be split
Splitting is the right move when a task hides more than one piece of work, or when its result cannot be judged until something unrelated also finishes. Three triggers come up repeatedly in the guidance.
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Several independent actions in one instruction
Microsoft Learn’s guidance on writing effective instructions for declarative agents, which is aimed at people building declarative agents, advises breaking multi-action instructions into clearly separated units. The stated reason is that this “reduces ambiguity and prevents the model from merging or reinterpreting tasks.” A common example is an instruction that asks the agent to extract metrics and summarize findings in a single step. The agent may not know which half to do first, or how to tell whether the first half is complete. Two separate tasks, each with its own completion signal, avoid that.
A hidden decision that must come first
Some tasks look like execution but contain a choice. Choosing between two data sources, deciding whether a migration should preserve an old field name, or picking which library to use are decisions. Downstream work cannot be planned properly until they are made. Give the decision its own task, with its own output, so the next step can depend on it explicitly.
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An outcome that cannot be checked yet
If a task can only be verified after unrelated work completes, failures become hard to locate. When a check fails, you cannot tell which part caused it. Snowflake’s planning article, which is a vendor publication rather than official guidance, describes planning as decomposing a goal into dependent subtasks and tracking intermediate state. Splitting along those dependencies lets each piece be checked as it finishes.
When work should stay together
Over-splitting is a common failure. Keep actions in one task when they serve a single outcome, share the same context, and can be checked as one unit. Splitting them would force the agent to rebuild the same setup several times and would lose the shared context that makes the work coherent. The aim is work that can be executed and verified, not the largest possible number of plan items.
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Sizing coding tasks against the actual project
For implementation work, the plan has to be anchored in the codebase. Visual Studio Code’s guidance on planning work with agents, on a page dated 30 September 2026, puts it directly: “Your actual plan should identify the files and code patterns in your project, not just list generic tasks.” The same guidance recommends reviewing whether a plan covers the requested scope, fits the project, surfaces decisions, and includes verification.
Illustrative example, not a benchmark:
| Version | Task text | Why it works or fails |
|---|---|---|
| Too broad | Add export support. | No outcome format, no affected files, no completion test. |
| Right-sized | Add a CSV export action to the reports controller, reusing the existing row-formatting code. Verify header order, an empty result set, and an invalid date range. | One outcome, named location, and checks that map to the behavior requested. |
Verification checks should match the change. Typical coding checks include tests for valid and invalid inputs, handling of empty results, and confirmation that existing behavior still works. The exact list depends on the requested change, so the check belongs in the task, not in a general closing step.
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When decomposition needs more layers
Some work is too open-ended for a flat list. Google Cloud’s Architecture Center describes a hierarchical task-decomposition pattern for ambiguous problems that involve research, planning, and synthesis. In that pattern, a higher-level agent breaks the goal into sub-problems and delegates them.
The extra layer has real costs, and the Google Cloud guidance names them: more model calls, higher latency, greater operational expense, and more architectural complexity. In practice, the layer is worth adding when:
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- the number of sub-problems is not known until the work begins;
- the sub-results must be synthesized into a single answer;
- the work is too large for one agent context to hold coherently.
For a well-defined task, such as the CSV export above, a flat plan is usually the better choice.
Should an agent plan stay fixed once generated?
No. A plan is an initial hypothesis about how to reach the goal. GOV.UK’s “AI Insights: Agentic Workflow” describes agent workflows as iterative, with monitoring and feedback allowing the system to adapt its plan. Snowflake describes the same loop: inspect the result of each step and revise later steps when observations change the path forward.
Plans should be revised when intermediate results reveal a new dependency, when an assumption turns out to be false, or when conditions change. Checkpoints after each meaningful outcome limit how far an agent can travel on a wrong assumption.
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A decision table for borderline cases
When you cannot decide whether to split or combine a candidate task, test it against the seven axes below. These are a practical synthesis of the guidance above, not a published scoring system or a numeric standard.
Quick Recap
| Axis | Question to ask | Lean toward splitting when | Lean toward keeping together when |
|---|---|---|---|
| Outcome clarity | Is there one understandable result? | The task would produce several distinct results. | One result is clear. |
| Actionability | Can the agent act without inventing goals? | A hidden choice must be made first. | The approach is already determined. |
| Dependency structure | Does it rely on a prior result? | Parallel actions should stay distinct. | Steps depend on each other tightly. |
| Verifiability | Can completion be checked on its own? | Checking needs unrelated work to finish. | One check covers the whole unit. |
| Context reuse | Would splitting repeat setup? | Each part needs little shared context. | Splitting would repeat setup or lose context. |
| Replanning cost | How costly is a wrong assumption? | The work is uncertain and checkpoints would catch errors early. | The work is predictable and low-risk. |
| Coordination overhead | Would extra layers add cost without control? | The work is ambiguous and open-ended. | Extra hierarchy would add calls, latency, and cost without better control. |
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