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Delegate work to an AI agent only when you can define the task, limit what it may do, and check whether it succeeded. Start with tasks that are repeatable and whose mistakes are visible; specify the goal, boundaries, evidence, and approval points; then review both the result and the agent’s execution. The person or organization using the output remains accountable for how it is used.
1. Decide whether the task is suitable for delegation
Break the larger goal into distinct subtasks before deciding what an agent should handle. A task that looks simple as a whole may contain a high-stakes decision or an action that is difficult to reverse.
- Repeatability: Does the task follow a pattern that can be described and applied consistently?
- Impact: What could happen if the result is wrong or incomplete?
- Error detectability: Can a reviewer check the result against source material or the actual state of a system?
- Time for review: Is there enough time for a person to check the result before it is used?
Routine status drafts and meeting-note summaries may be reasonable candidates for AI assistance with review. A budget approval or an external communication that requires judgment calls for human ownership of the decision. These are examples, not guarantees that a particular task is safe. Microsoft’s task-selection guidance uses these factors to help distinguish work that can be automated with review from work that should remain human-led.
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2. Write a delegation contract before the agent starts
A useful handoff makes success observable. State what the agent should produce, what it may use or change, and what evidence will show that the work is complete. Google Cloud describes breaking work into tasks that can be reliably verified; its guidance also recognizes that subjective quality judgments may still require an expert. AWS similarly recommends atomic capabilities, input validation, structured output schemas, clear handoff schemas, and versioned instructions. These practices reduce ambiguity; they do not guarantee correctness.
Include these elements in the handoff:
- Goal and completion criteria: Name the deliverable and the conditions that make it done.
- Scope: Identify included work, out-of-scope work, and any point where the agent must stop.
- Context and sources: Provide the relevant material and specify which sources should support factual claims.
- Allowed tools and actions: State which data and tools may be accessed and which actions are permitted.
- Output format: Define the structure, fields, or schema the response must follow.
- Evidence: Require links to supporting material, calculations, tool results, or a record of changes where appropriate.
- Escalation conditions: Say when the agent must ask a person for clarification or approval rather than guessing or proceeding.
For example, a handoff for summarizing a set of meeting notes could ask for a summary, decisions, and assigned actions; limit the agent to the supplied notes; require each action to be tied to the relevant note; and instruct it to flag unclear owners rather than infer them. The checkable criteria make it possible to review the output against the source instead of relying on the agent’s claim that it is finished.
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Decompose larger work until each unit has a clear input, output, and check. Google Cloud’s article, “How agents can delegate better,” calls this contract-first decomposition. For structured tasks, schemas can make missing or malformed fields easier to spot; for work involving judgment, define a rubric and identify which decisions need expert review.
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Pick the simplest workflow that fits how the work is connected. A more elaborate arrangement can add implementation and maintenance work without improving a task that has a fixed, straightforward sequence.
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| Work shape | Pattern to consider | Trade-off |
|---|---|---|
| Steps have a known order and depend on earlier results | Sequential agents or a deterministic workflow | Easier to reason about, but each step depends on prior outputs. |
| Subtasks are independent and can proceed at once | Parallel agents | Can reduce latency, but outputs still need consolidation and checking. |
| Drafting or analysis is open-ended and benefits from revision | Iterative refinement, such as a draft followed by a critic | Repeated passes can improve quality, but add latency and depend on good reasoning and clear exit criteria. |
| An action has high impact, requires subjective judgment, or needs final approval | A human-in-the-loop checkpoint | Requires an interaction and implementation overhead, while placing human judgment at a critical decision point. |
Consider dependency structure, opportunities to parallelize, how much refinement is worthwhile, latency tolerance, maintenance complexity, and how well the workflow can be observed. Google Cloud’s architecture guide to agentic AI patterns describes these workflow shapes and their trade-offs. A critic agent can offer another pass, but it is not a substitute for independent evidence or human review of a consequential decision.
4. Set permission limits and approval checkpoints
Give the agent only the access needed for its assigned task. Define permitted actions, actions that require approval, and actions it must not take. Make approval rules before execution: the operator or system designer should decide where a person must intervene, rather than leaving the agent to decide whether its own work deserves approval.
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Set checkpoints according to impact, reversibility, and the likelihood that a mistake will escape detection. For example, preparing a proposed change can be separated from applying it; an irreversible or destructive action, such as deleting important records, is a strong candidate for explicit human approval. AWS’s Agentic AI Lens guidance and the Australian Cyber Security Centre’s careful-adoption guidance address bounded capabilities and oversight.
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5. Verify the result and inspect how it was produced
Do not treat a completion message as proof. Check the deliverable itself, then inspect the execution path for failures that may not be obvious in the final response.
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Check the result
- Compare factual claims with the cited or supplied source material.
- Recalculate important figures from the original data rather than accepting a reported calculation on trust.
- Confirm claimed changes against the actual system state.
- Use a rubric for subjective work, and route judgment-heavy decisions to a qualified person.
- Check that the requested output fields and completion criteria are present.
Check the workflow
- Review which tools were used and what they returned.
- Follow handoffs between steps to see whether important context or constraints were lost.
- Check whether required guardrails and human-review steps actually occurred.
- Look for tool-choice, instruction-following, handoff, or safety failures, not just errors in the final prose.
OpenAI’s agent evaluation guide recommends inspecting traces when debugging workflow behavior and using structured graders to detect failures such as incorrect tool choices, handoffs, instruction-following, or safety behavior. After defining the quality target, use datasets and repeatable evaluation runs to compare changes to prompts or routing. A passing evaluation is evidence about the cases tested, not proof that every future run will be correct.
NIST describes a developing evaluation-probe project that compares agent claims with a human-curated reference corpus and creates machine-readable audit trails connecting decisions to supporting evidence. The project page, updated May 5, 2026, says: “Our evaluation probes scrutinize the factual grounding of AI outputs as they are produced by comparing them against a human-curated corpus of reference documents.” See the NIST project page. This approach concerns factual grounding; it is not a guarantee that automated probes can verify every type of work.
6. Keep a person responsible for using the output
Decide who will review and approve the result before the workflow begins, especially when the work affects people, money, records, or external communications. That person should have access to the evidence needed to make the decision, not just the agent’s summary of its own performance. Microsoft states, “Delegating work to AI doesn’t transfer accountability.” Its guidance says people remain responsible for reviewing, validating, and approving how outputs are used. Delegation can move work to an agent; it does not move responsibility for the decision to use the result.
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