Technology executives expect AI agents to become important quickly; the IT practitioners responsible for making them work are far less certain. A 2025 PagerDuty survey, reported by CIO, found that 53% of technology executives expected agents to be core to business operations within two years, compared with 29% of IT practitioners. That gap reflects more than resistance to change: practitioners are closer to the integration, testing, security, and maintenance work that turns a promising demo into a dependable production system.
What explains the gap between executive optimism and practitioner confidence?
The figures describe expectations, not a count of organizations already running autonomous agents. In the same PagerDuty survey, 38% of all respondents expected agents to be core to business operations within two years, while 50% expected them to play a peripheral role. The distinction matters: respondents could see a place for agents without expecting them to become central to operations.
Executives may be evaluating strategic potential; practitioners must evaluate what happens when a system encounters inconsistent data, a legacy application, a security boundary, or an exception that was not anticipated in a demo. Tim Armandpour, PagerDuty’s CTO, put the difference in visibility this way: IT managers and staff are “closer to the action and closer to the madness of what it takes to operate and manage things at scale.”
Why are IT practitioners skeptical of AI agents?
“Agent” can describe a less autonomous system than the label suggests
Many products marketed as agents still rely on highly deterministic, prescribed workflows. They may automate a sequence of steps, but that is different from an agent that independently selects processes and tools, adapts to new situations, and learns from the results. Maya Mikhailov, CEO of Savvi AI, says that more autonomous future state is not yet the norm; current workflows still need substantial human customization and upkeep.
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Teams have to prepare data, define acceptable behavior, test ordinary and unusual cases, monitor results, and maintain integrations as systems change. Mikhailov says, “Frontline AI practitioners have likely seen the amount of customization, quality assurance, and maintenance required to make a somewhat functional agentic workflow.” That labor can be easy to miss when a vendor demonstration shows only the successful path.
Pull Logic’s testing experience illustrates the problem: inconsistent manufacturer catalog descriptions led an agent to hallucinate product numbers and features. A system that sounds plausible is not necessarily accurate enough to act on; teams need to validate outputs against authoritative data and decide what the system should do when it cannot.
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Accuracy is only one concern. Rahul Chahar, co-founder and CTO of Pull Logic, says, “IT practitioners are cautious due to concerns around accuracy, transparency, security, and integration complexities.” He also describes the practical combination: “Agentic AI systems often prove unpredictable, tough to troubleshoot, and challenging to mesh with older infrastructure, not to mention the compliance and security headaches they can create.”
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These are not separate boxes to check once. A change to a model, prompt, connected tool, or source system can affect behavior and create new testing, access-control, and compliance questions. Practitioners who will own those consequences have reason to ask for evidence and operating plans before a broad rollout.
Are AI agents ready for enterprise deployment?
Readiness depends on the task and the consequences of an error, not on a blanket yes or no. A bounded workflow with clear inputs, measurable outcomes, and a human review point may be a sensible pilot. An agent with broad permissions, ambiguous goals, sensitive data, and little observability is a much harder production proposition.
Before deployment, leaders and practitioners should agree on:
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- The problem: What specific operational pain is the agent meant to reduce, and how will the team tell whether it helped?
- The boundary: Which systems, data, and actions can it access, and which decisions require human approval?
- The evidence: How will the team test accuracy, exceptions, security, and behavior after changes?
- The owner: Who is accountable for incidents, updates, access, and ongoing performance?
- The fallback: How can staff pause the agent, correct an error, or complete the work another way?
The survey figures do not establish a productivity gain, return on investment, or the number of enterprises operating fully autonomous agents. They show a difference in expectations. Organizations should make adoption decisions from their own workflow evidence rather than treating enthusiasm or skepticism as proof of performance.
How can CIOs roll out agents without losing IT staff trust?
Start with a practitioner-defined problem
Choose a specific, high-value task that employees already understand, rather than introducing an agent because the technology is available. Chahar advises that “CIOs and CTOs need to align agentic AI initiatives with their teams’ real challenges.” Involve the people who handle the work in choosing the use case, defining failure conditions, and setting the measure of success.
Keep the first pilot small and reviewable
Rob Kim, CTO at Presidio, recommends small, high-impact projects. Limit the pilot’s scope and permissions, keep a human in the loop where errors matter, and make the agent’s actions visible to the people responsible for the process. A pilot should produce useful evidence about accuracy, exceptions, workload, and maintenance—not merely show that the workflow can run once.
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Make feedback and accountability part of the rollout
Explain what the agent can and cannot do, how its outputs are checked, and who is responsible when something goes wrong. Provide training that fits the actual workflow and give practitioners a route to report problems without being treated as blockers. Kim says, “Create regular forums for practitioners to share insights and challenges with leadership.” Their observations should feed into decisions to revise, expand, or stop a deployment.
Kim also offers a useful expectation-setting point: “This will be the worst version of agents and agentic AI that you will ever use — it only gets better.” That is a reason to plan for iteration, not to assume improvement will happen automatically. Changes need testing and oversight, and a pilot that fails its agreed criteria should not be scaled on optimism alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does it take to manage AI agents in production?
Managing a growing set of agents is an operational-control problem, not just a sequence of isolated experiments. A March 2025 CIO report describes the need for a consolidated view and tools for monitoring and governance. As deployments multiply, teams need to know what agents exist, what they can access, which versions are running, and how they are performing.
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- Inventory and versioning: Keep a record of agents, owners, versions, connected tools, and changes so teams can identify what is running and investigate unexpected behavior.
- Performance and alerts: Monitor outcomes and surface poor performance or security concerns promptly; establish who receives alerts and what action follows.
- Cost tracking: Attribute costs to agents and use cases so expanding usage does not become invisible.
- Authentication and access: Control how agents identify themselves and restrict their permissions to what the workflow requires.
- Security and governance: Set policies for data access, acceptable actions, review, incident handling, and compliance, then check that deployed systems follow them.
AgentOps and OneReach.ai are among the vendors named in the March report as entering this emerging management market. The important requirement is the capability, not a particular product name: enterprises need sufficient inventory, observability, access control, cost visibility, and governance to manage the agents they actually deploy.
The practical takeaway
Executive optimism is about what agents might make possible; practitioner caution is about whether a particular system can do a particular job safely and reliably in a real environment. The gap narrows when organizations choose concrete problems with their IT teams, run bounded pilots, expose failures and operating costs, and build the controls needed to manage systems after launch. Trust comes from seeing that the work and the accountability are shared—not from asking practitioners to accept a promise.
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