Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteDelegate bounded tasks with clear completion criteria, limited permissions and evidence you can check. Choose work that is repeatable and whose errors are visible; match the workflow to the task’s dependencies; inspect both the result and the agent’s execution; and require human approval before consequential or hard-to-reverse actions. The person or organization using the result remains accountable for reviewing and approving it.
Which work is appropriate to delegate?
Assess each subtask—not just the overall project—against four questions: how repeatable it is, how much harm an error could cause, how easily a reviewer can detect an error, and whether there is enough time for meaningful review. These criteria help distinguish work that can be automated with review from work that should remain human-led. Microsoft’s task-selection guidance uses this kind of risk-based decision rather than treating every task as equally suitable for an agent.
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- Good candidates for delegation with review: routine, repeatable work with checkable outputs, such as drafting a status update from supplied notes or summarizing meeting notes against a transcript. These are examples, not guarantees that the output is safe or correct.
- Keep a person closely involved: tasks with subtle errors, high impact, subjective judgment, or no time to inspect the result. An agent can prepare material for a decision, but that does not make the decision itself appropriate to delegate.
- Do not delegate execution without a review plan: if you cannot explain how a reviewer would detect a bad result, first add a reliable check, narrow the task, or keep it human-led.
Consider impact and reversibility together. A mistake that is easy to find and undo needs a different level of oversight from one that could harm someone, create an external commitment, or permanently change important records. There is no universal numerical threshold for acceptable autonomy or review frequency; set the threshold for the task and its consequences.
How do you make the handoff testable?
Before the agent starts, write a delegation contract that makes both the work and its limits observable. Break the larger goal into units simple enough to monitor and verify. Google Cloud describes this as contract-first decomposition: tasks should be broken down until their completion can be checked, while recognizing that subjective quality may still need an expert. Google Cloud’s delegation guidance also emphasizes reliable verification. AWS recommends atomic capabilities, input validation, structured output schemas, clear handoff schemas and versioned instructions; those practices reduce ambiguity but do not guarantee correctness. AWS Well-Architected Agentic AI Lens
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Include these terms in the contract
- Goal and definition of done: state what the agent must produce or change and what observable evidence will count as completion.
- Scope and exclusions: identify the permitted task boundaries and explicitly name actions or topics that are out of scope.
- Context and inputs: provide the relevant source material, records, assumptions and date or version where that matters. Distinguish authoritative sources from background information.
- Tools and authority: specify which tools, data and actions are permitted, and which actions require approval or are prohibited.
- Output format: request a structure a reviewer can inspect, such as a table with source references, a list of proposed changes, or a defined schema when downstream processing depends on it.
- Evidence and checks: require links or references to supporting material, tool results, calculations, or a before-and-after record as appropriate.
- Escalation conditions: tell the agent when to stop and ask a person—for example, when required information is missing, sources conflict, the task exceeds scope, or an approval gate is reached.
A reusable handoff example
Adapt this plain-language structure to the task rather than treating it as a guarantee of performance:
Goal: [specific result to produce]
In scope: [allowed work and sources]
Out of scope: [actions or decisions not allowed]
Tools and permissions: [permitted access and actions]
Output: [required format and fields]
Completion checks: [evidence a reviewer can verify]
Stop and ask a person if: [ambiguity, conflict, risk, or approval condition]For example, a request to summarize meeting notes can require a concise summary, action items tied to named speakers where the transcript supports them, and a reference to the relevant source passage. It should also say not to infer missing decisions. This makes omissions and unsupported claims easier to spot than a request for a general “meeting summary.”
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Which workflow pattern fits the work?
Choose a pattern based on dependencies, the value of parallel work or revision, latency tolerance, implementation and maintenance complexity, observability, and where human judgment is needed. The patterns below are described in Google Cloud’s agent architecture guidance; none is best for every task.
| Work shape | Pattern to consider | Trade-off to plan for |
|---|---|---|
| Known steps with fixed dependencies | Sequential agents or a deterministic workflow | Easier to reason about, but each step depends on the outputs before it. |
| Independent subtasks that can proceed at once | Parallel agents | Can reduce latency, but someone or something still needs to consolidate and check the results. |
| Open-ended drafting or analysis that benefits from revision | Iterative refinement, such as a draft followed by a critic | Repeated passes can add quality, but they also add latency and depend on sound reasoning and clear exit criteria. |
| High-impact action, subjective judgment, or final approval | Human-in-the-loop checkpoint | Adds interaction and implementation overhead while placing human judgment at a critical decision point. |
A critic agent can identify issues, but its review is not independent evidence and does not replace human review for a high-consequence decision. A human-in-the-loop workflow should pause at a predefined checkpoint so a person can review, correct, approve, or supply information.
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How do you verify the result and the agent’s execution?
Use two separate checks. A polished answer or a completion message is not proof that the task was completed correctly, and a correct-looking result does not show that the workflow respected its boundaries.
Check the result against evidence
- Compare factual claims with the cited source material, not just the agent’s summary of it.
- Recalculate important figures from the original data and verify units, assumptions and inputs.
- For claimed system changes, inspect the actual system state or change record rather than relying on the agent’s description.
- For subjective work, use a defined rubric and route judgment-heavy decisions to a qualified person.
Check the workflow trace
Inspect the available trace or audit record for the tools used, their outputs, handoffs between steps, guardrails and any required review gates. Look for unexpected calls, missing evidence, skipped approvals or an agent continuing after an escalation condition should have stopped it. OpenAI’s agent evaluation guidance describes using traces to debug workflow behavior and structured graders to detect issues such as tool-choice, handoff, instruction and safety failures.
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Once you understand the quality target, evaluate representative runs against explicit criteria. A repeatable dataset and evaluation run make it possible to compare changes to prompts or routing on a consistent basis; a single successful example does not establish that a workflow is reliable across cases.
NIST’s project on evaluation probes describes comparing agent claims with a human-curated corpus and creating machine-readable audit trails that link decisions to supporting document evidence. This is a developing project, not a general guarantee that automated probes can verify every kind of work. NIST’s project page, updated May 5, 2026.
Where should permission limits and human approval go?
Set authority before execution. Give the agent only the access needed for the task, and make the boundary explicit: what it may read, draft, change, send, publish, approve or delete. AWS recommends atomic capabilities and clear oversight arrangements; its guidance supports designing these controls as part of the workflow.
Require approval where impact is high, a decision is subjective, or an action is difficult to reverse. For example, a workflow can let an agent prepare a proposed update while requiring a person to approve it before publication, or pause before deletion of important records. Australian Cyber Security Centre guidance discusses careful adoption and oversight of agentic AI services.
Approval rules should be defined by the system designer or operator, not left to the agent to decide when its own work is important enough to review. Keep a record of who approved a consequential action and what evidence they reviewed. Delegating execution does not delegate accountability: the person or organization using the result remains responsible for reviewing, validating and approving how it is used. Microsoft’s guidance on task selection and accountability
Quick Recap
A practical review checklist
- Is this subtask repeatable, and are its likely errors detectable?
- Have you defined the goal, boundaries, inputs, permitted tools, output format and completion evidence?
- Does the workflow fit the dependencies and need for parallel work or revision?
- Have you checked source evidence or actual system state, rather than only the agent’s claim?
- Does the trace show permitted tool use, valid handoffs and required approvals?
- Are consequential or hard-to-reverse actions paused for human approval?
- Is a named person or team responsible for validating and approving use of the result?
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