Why Your AI Agent’s Job Description May Not Predict Its Performance

Hanah-Marie Darley, Co-Founder and Chief AI Officer, Geordie.

gettyEnterprise leaders see plenty of advice on AI agents, including defining skills, operationalizing adoption and getting more from the technology. Yet, one question often remains unanswered. How do agents actually behave once they begin working across the enterprise?

To understand agents, leaders need more than a description of their purpose, configuration and permissions. They need to see how agents make decisions, use tools and respond to context in real work.

No single system was designed to explain an agent’s complete operation. Identity systems show access. AI platforms show configuration. Cloud and application tools show pieces of activity. Each produces useful evidence, but rarely a shared view of what an agent was trying to do, how it proceeded and whether its work delivered the intended result.

The executive challenge is to give the teams approving, operating and relying on agents a shared basis for deciding what to expand, refine or stop.

As agents take on work across systems, leaders need an operating picture that connects an agent’s role and permissions with its decisions, resources and outcomes. Behavioral context turns those separate signals into evidence of how effectively an agent is performing.

The employee analogy is useful, provided we do not push it too far. Agents are not people, but in both cases, a role description only tells us so much about performance in changing circumstances.

The distinction I keep returning to is simple. Architecture shows how an agent is built. Configuration shows what it can do. Behavior shows what it actually does when it works.

Design-time information is a starting point. It cannot show every decision an agent makes in a changing operating environment. A retry can become a loop, and context can change the systems, data or human review a task involves.

Consider a customer-service agent authorized to query order systems, issue standard refunds and escalate exceptions. Its configuration may remain unchanged. Yet, a shift in demand, a new tool or a pattern of ambiguous requests can change the work it does, the decisions it makes and the outcomes customers receive.

Behavior supplies that missing context. It shows the sequence of actions behind a result and helps teams see where an agent is dependable, where a workflow needs support, and where design-time assumptions should be revisited. Over time, it shows how an agent operates in practice, rather than only what it was intended to do.

For senior leaders, this is as much an organizational question as a technical one. Behavior gives teams a shared object of discussion: the work an agent performed, the context that shaped it and the outcome it produced.

Most enterprise operating models rely on periodic snapshots, such as what software is, where it runs, what it can access and whether it meets relevant standards at a given moment. Those checks remain important, but they cannot explain how an agent performs as it makes decisions, accumulates context and carries out work.

• Architecture: Understand the agent’s purpose, identity, models, tools, data sources and dependencies.

• Configuration: Know the agent’s boundaries, systems and available actions.

• Behavior: Observe how the agent works across real tasks, including how it uses tools, responds to changing context and develops patterns over time.

The employee analogy is useful here. We would not evaluate someone solely through a role description or periodic checkpoint. Similarly, an agent’s configuration tells us what it is expected and permitted to do, but its behavior reveals how it performs under changing circumstances.

Snapshots start the operating picture. Behavior completes it. Together, these layers help leaders decide where an agent can take on more responsibility, where its work needs refinement and where intervention is needed before a problematic pattern becomes embedded in an important workflow.

Executives can begin with a practical set of questions. What is this agent meant to achieve? What capabilities and resources are available to it? How is it using them in practice? Which recurring patterns support the intended outcome? How should its performance be evaluated as the agent, its context and the systems around it evolve? Where is the organization learning something that should change the agent, the workflow or the decision around it?

The crucial capability is that the organization can connect the evidence and make decisions with it.

Every task produces more than an output. It can produce evidence about how the enterprise is operating through agents. Organizations that learn from that evidence will be better positioned to improve workflows, allocate responsibility and deliberately extend autonomy.

The opportunity is to learn from agents as they work, then use that understanding to decide where more responsibility is warranted. ​

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