Artificial Intelligence
How to Manage AI Agents
As AI agents expand across the enterprise, active management is essential for safe scaling and sustained value.
Sydney Scott
Editorial Strategist, AI
Workday
Agents have ushered in a new era of enterprise AI. More than 80% of leaders reported to Workday that they’re already deploying agents in some capacity, and Gartner predicts that 4 in 10 enterprise applications will have agents built into their software within the next year.
As agentic AI systems become embedded into enterprise workflows, knowing how to manage AI agents directly affects risk, trust, and value. Human leaders are responsible for setting the direction and boundaries that guide agent behavior.
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How to Manage AI Agents: 5 Key Steps
Workday research shows that clear AI agent management increases employee willingness to embrace agents. Managing AI agents successfully requires deliberate choices outlining how they’ll operate inside business workflows and work alongside employees.
The 5 steps below outline a practical approach for how best to manage AI agents:
1. Define Clear Objectives and Decision Scope
To maintain human oversight, record your AI agent's instructions in a shared runbook or configuration file. Be specific: include the exact goals you want the tool to achieve and the criteria it must meet before the task is considered complete.
For example, a clear agent mandate would be: “Route and validate invoices, prepare them for approval within three business hours, and flag any exceptions that exceed defined error thresholds.” Avoid vague statements like “automate invoice processing,” which can lead to agents acting outside of their intended scope.
Boundaries, defined by our choices, are essential for purposeful AI agent deployment.
Kathy Pham, VP, Open Technology and AI, Workday
2. Establish Guardrails and Escalation Paths
Once an agent has authority to act, you also need to be clear about when it must pause and hand work over to a person. Define the situations where human involvement is required so the agent doesn’t push forward when judgment or accountability is needed.
Escalation should be just as deliberate. Decide who the issue goes to and what information they need to resolve it. When agents surface their recommendation and the reasoning behind it, people can step in quickly and keep work moving.
3. Coordinate Human and Agent Workflows
Once guardrails and escalation paths are in place, define how work proceeds the rest of the time. Spell out which steps agents handle as part of normal execution and where humans are expected to review or approve work by default.
This removes ambiguity during day-to-day operations. Teams then know when agents move work forward on their own and when human involvement is a part of the standard workflow.
4. Orchestrate Agents Across Systems
Coordination is key when AI agents work across multiple systems. One automated action can trigger unexpected changes elsewhere, which is why human oversight is necessary to prevent duplicate work or conflicting decisions.
To maintain human oversight, assign a lead to monitor your AI workflows. This person ensures that the hand-offs between different systems are smooth and approves any changes to the automation process to prevent conflicting actions.
When an agent is updated, added, or removed, this owner checks downstream impacts and adjusts related agents before changes go live.
5. Monitor Outcomes and Refine Behavior
Once an agent is live, it’s key that you verify it’s delivering the outcome you designed it for and to correct course when it isn’t. That means reviewing a small set of business-level measures on a regular cadence and using them to decide whether the agent’s behavior needs to change.
Focus on metrics that show whether the workflow is actually improving:
- Cycle time: Is the agent optimizing the process it supports?
- Quality: Are outputs meeting standards without added review or correction?
- Cost: Is total effort being reduced, not just redistributed?
- Employee experience: Are teams spending less time on routine tasks and more time on higher-level work?
When results drift, change the agent’s mandate, decision limits, or review points. Repeat this process whenever workflows or priorities change so that agent behavior reflects how the business operates in the moment.
83% of workers trust their organization to use AI for their benefit, as long as the boundaries are clear.
Why Do AI Agents Need Human Oversight?
Many teams are excited about AI agents: 88% of employees say they believe agents can ease their workloads and boost productivity. The primary benefit of AI-driven tools is their ability to operate independently. After the initial setup, they execute routine processes with very little human intervention.
But while AI tools don't require constant supervision, they do need clear boundaries to be effective. Smart oversight guides how the AI operates, ensuring it stays accurate, accountable, and helpful to your business.
- Authority stays human: Employees accept AI agents as collaborators, not leaders. While 75% say they’re comfortable working alongside AI agents, only 30% are comfortable being managed by one. Even as AI agents become more integrated in daily work, acceptance stops short of authority.
- Alignment requires direction: AI agents are relentless optimizers, pursuing goals even as conditions change. Without human direction, that focus can drift away from what the organization actually wants to achieve. Ongoing oversight keeps agents focused on evolving priorities and ensures that productivity gains remain tied to strategic intent.
- Judgment can’t be delegated: No matter how sophisticated agents become, certain human skills remain out of reach. Ethical decision-making and moral judgment consistently rank among the skills leaders believe AI cannot replace, and they are a core reason active agent management remains essential.
- Accountability supports scale: Confidence in using AI responsibly skyrockets from 36% in the early stages to 95% once a company is using it successfully. When AI agents operate across multiple workflows, human oversight allows organizations to automate more tasks efficiently while ensuring that every decision is still backed by a person in charge.
Final Takeaways: Leading in an Agentic AI Future
As AI agents take on more responsibility across enterprise workflows, leaders must be explicit about where authority lies and how decisions are governed when work is no longer fully human. Without that clarity, it’s impossible to build trust with your employees.
When AI agents are managed with the same rigor applied to financial or operational performance, agents can scale safely as collaborative execution support. This allows organizations to benefit from agents’ autonomy and efficiency while keeping complex judgement and final decisions firmly in human hands.
A remarkable 82% of organizations are already using AI agents. But is your team ready? Read our latest report to learn how businesses are maximizing human potential with AI.
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