How do you manage AI agents like employees? Give each agent a defined role, a named human owner, limited authority, measurable outcomes and ongoing review. Borrow the discipline of employee management—not the idea that software is a person. The organization and its people remain accountable for what an agent does.
Why an agent needs more than an output score
An agent may act through tools and workflows, not just generate a single answer. A polished result can conceal excessive access, a policy breach or a process that cannot be reconstructed afterward. Performance management therefore needs to assess the agent’s actions and controls as well as its final output.
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Think of performance as a system: the agent’s role, tools, workflow, real-time controls and organizational governance all interact. Google Research’s listing for Sandeep Saini’s 2026 article describes an Agentic Operating Model built around cognitive specialization, coordination architecture, real-time control and organizational governance. Its central implication for managers is practical: a capable model can still fail when the surrounding system is misaligned. Google Research publication listing
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What to borrow from employee management—and what not to
Employee-management practices can make agents easier to understand and oversee: define the job, identify an owner, set boundaries, review performance and plan for updates or retirement. KPMG’s 2025 framework proposes a lifecycle of onboarding, training, performance management, continuous learning and retirement, supported by agent identities, reporting lines, accountable owners and a system of record. It is an implementation framework from a consultancy, not a proven universal standard. KPMG’s 2025 report
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The analogy has limits. An agent is software, not a human colleague who can bear responsibility. A human owner should be answerable for its scope, and the organization remains responsible for how the system is deployed. Before launch, document:
- Role: the work the agent is intended to perform and the outcomes it should produce.
- Owner: the person responsible for configuration, review and follow-up.
- Authority: the tools, data and actions the agent may use, including actions requiring human approval.
- Record: where instructions, permissions, changes and relevant activity can be reviewed.
How to evaluate agent performance
Use a role-specific scorecard rather than relying on a single model benchmark or a general impression. KPMG recommends tracking reliability, accuracy, compliance and contribution to value. Those measures become useful when each has a clear definition tied to the agent’s actual job.
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| Measure | What a manager should assess |
|---|---|
| Reliability | Whether the agent performs its assigned task consistently under the conditions in which it is meant to operate. |
| Accuracy | Whether outputs and actions are correct for the task, with checks suited to the consequences of mistakes. |
| Compliance | Whether the agent follows its permissions, instructions and applicable organizational requirements. |
| Value contribution | Whether its work contributes to the intended outcome in the role and context for which it was deployed. |
Set evaluation criteria before deployment and revisit them when the role, tools or operating context changes. A measure that matters for one task may not capture another; there is no universal scorecard established for every agent.
How to set authority and human oversight
Give an agent access only to what its task requires, use a verified identity, and preserve records that let a human understand what it did and who is responsible for follow-up. Scale oversight to both autonomy and consequence: the more independently an agent can act, and the more costly a mistake could be, the stronger the safeguards and human review should be.
A World Economic Forum report with Capgemini, published on 27 November 2025, proposes classifying agents by function, autonomy, predictability and context, then applying progressive safeguards and continuous monitoring. It reports that 82% of executives planned to adopt agents within the next one to three years; that is an executive intention figure reported in that 2025 report, not a general adoption rate or evidence that adoption occurred. World Economic Forum and Capgemini report
Use these questions to match controls to the work:
- Can the agent act on its own, or does a person approve consequential steps?
- Are its data and tool permissions restricted to the assigned task?
- Can reviewers reconstruct its actions and investigate a concern?
- Does the potential harm from an error justify additional checks or escalation?
Monitoring itself needs safeguards. OpenAI’s March 2026 account describes an internal system that reviews coding-agent interactions, flags behavior that may conflict with user intent or internal policy, and sends concerns for human triage. OpenAI reported that its then-current system reviewed concerns within 30 minutes and had flagged every interaction employees independently reported through internal channels. These are organization-reported results from an internal example, not an independent assessment or a general guarantee. Reviewing conversations, tool calls and outputs also raises privacy and data-security considerations. OpenAI’s account of internal coding-agent monitoring
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to run a performance-management cycle
- Onboard: establish the agent’s identity, role, owner, permissions and recordkeeping before it begins work.
- Set expectations: define role-specific measures for reliability, accuracy, compliance and contribution to value.
- Monitor use: review activity and outcomes, with human attention proportionate to autonomy and consequence.
- Review and adjust: use performance results, incidents and near misses to change instructions, permissions, safeguards or evaluation criteria.
- Update or retire: treat changes to the agent and its operating context as part of its lifecycle; retire it when it no longer fits the work or controls available.
For the broader organizational picture, an OECD working paper published in September 2026 examines how organizations develop and deploy agentic AI, including benefits, challenges and governance, using practitioner interviews across regions and sectors. Its landing-page abstract establishes the study’s scope but does not provide detailed findings to support specific prevalence or outcome claims. OECD working paper
Quick Recap
What a manager should remember
- Define each agent’s job, boundaries and human owner before deployment.
- Evaluate observable results and behavior against role-specific criteria.
- Limit access, keep reviewable records and scale oversight to risk.
- Use incidents and near misses to improve controls, and include updates and retirement in the management cycle.
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