Businesses Need to Manage AI Agents Like Team Members

Organizations deploying AI agents should manage them with defined roles, access limits and recurring performance reviews. Evaluations can expose both agent errors and flaws in the workflows they are meant to support. This approach could help businesses expand agent use while keeping responsibilities and escalation paths clear.

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The AI Maker

12/24/20262 min read

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As AI agents take on tasks such as vendor screening, customer support and meeting scheduling, organizations need to define their responsibilities and evaluate their performance, rather than treating deployment as a one-time technical project. Clear roles, regular reviews and feedback from real-world use can help businesses determine whether agents are doing useful work—and where processes need to change.

Agentic AI remains an emerging practice, with no established industry-wide standards or best practices. That leaves technology leaders to set their own operating rules while agents begin working alongside human teams. A practical starting point is to treat an agent as an assigned worker: specify its duties, the systems it can access and the limits of its authority.

For example, a retailer might assign an agent to handle customer-service cases for a particular product line. Its instructions should identify which products are covered, what issue should trigger action, what messages the agent may send and which manager should receive escalations. Those details help keep the agent’s work within defined boundaries and make its actions easier to assess.

Those boundaries may need adjustment as teams see how an agent behaves in practice. Organizations can collect performance data to check whether it follows instructions, completes tasks and escalates cases appropriately. The goal is not to assume that an agent will perform perfectly, but to establish a basis for identifying errors and deciding whether its performance is adequate for the assigned work.

Regular reviews can help teams make that distinction. A shortfall may be acceptable for a low-risk task, such as coordinating internal meetings, but less so when an agent’s actions could affect customer relationships or a company’s products. Where performance falls short, teams can use feedback and reinforcement learning to guide improvements, then check whether changes have produced the intended result.

Reviews can also reveal problems beyond the agent itself. Comparing the process leaders expect to happen with the process an agent actually follows may expose unclear instructions, missing tools or flawed workflows. In some cases, the agent may be assigned to the wrong task; in others, the documented process may not reflect how work is really done. That makes agent evaluation a possible source of operational insight, not just a model-quality check.

Current examples include agents that screen new suppliers, resolve routine support tickets and find meeting times. Observation agents can also monitor workflow bottlenecks and point to areas where training or automation may help. These responsibilities vary in risk and complexity, so organizations will need to match oversight and access controls to each role rather than apply a single standard to every agent.

As deployments expand, the central management question is not simply whether an agent can complete a task. It is whether the task is clearly defined, its outcomes can be reviewed and humans know when to intervene. Those operating practices could help organizations build confidence in agent use while keeping accountability and escalation paths visible.

Cited: https://www.techradar.com/pro/from-tools-to-teammates-ai-agents-in-action

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