As organizations move AI agents from pilots into production, the stakes quickly rise. An agent’s ability to complete a task in testing is important, but true readiness depends on how it performs when conditions change and decisions carry real business consequences.
For technology leaders, the challenge is distinguishing controlled-demo success from real-world reliability. Here, members of Forbes Technology Council highlight often-overlooked factors that can affect autonomous AI deployments and explain how missing those details can lead to risk, poor results or lost value.
Cost Controls For Autonomous Agent Workflows
Cost controls are vital. Even if you somehow manage to figure out how to get an agent to be predictable and accurate, what is the unit cost, and how do you manage the exposure? Companies are finding their AI agent spend on things like coding is coming in at two to three times budget estimates as models get more autonomous and do deeper, multistep reasoning. – Praful Saklani, Pramata
Security Guardrails For Real-World Threats
How an agent’s security guardrails perform in real-world adversarial environments is often a major oversight. Acting as the first line of defense, guardrails must be constructed to withstand today’s threats. Before adopting and offering access to AI agents, organizations need to learn which can withstand today’s adaptive attacks by ensuring data, assets and privacy are appropriately secure. – Peter Garraghan, Mindgard
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Business Context Behind AI Performance
The overlooked factor is business context: Accuracy and impact aren’t equal. Many benchmark agents on model performance, but real success is revenue, customer lifetime value and growth. Production readiness requires more than a capable model; it needs trusted data, domain expertise and guardrails. The goal was never autonomous AI for its own sake but AI that knows what winning looks like. – Chih-Han Yu, Appier
The Need For An AI Agent At All
AI agents are overkill for tasks where process steps are known, outputs are deterministic or goals are ill-defined. Missing that detail means subjecting ourselves to unnecessary complexity, higher maintenance costs and lots of extra risk. – Nicole Radziwill, Team-X AI (a unit of Ultranauts, Inc.)
Management Structures For Autonomous Agents
A commonly overlooked truth: Highly autonomous agents still need management, just like employees. Demos can look great, but production success requires trusted data, clear permissions, observability and recovery paths. An agent’s “manager” must set goals, review performance and refine instructions so outcomes stay consistent over time. – Hemant Kashyap, Kindsight
Outcome Improvement Beyond Workflow Speed
An overlooked factor in healthcare is whether technical autonomy actually improves outcomes, not just the speed of the process. AI agents may execute workflows efficiently, but if the workflow is flawed, poorly governed or clinically misaligned, autonomy only scales the wrong result faster. Production readiness should include evidence that the agent improves outcomes with clear safety guardrails. – Paige Kilian, Inovalon
The Full AI Operating System
Readiness isn’t about the agent itself. It’s about the whole system, including the model, the guardrails and how employees work with it. A sharp agent dropped into a setup with no escalation, no visibility and no ownership is not ready, no matter how good the initial benchmarks look. For example, does an agent hand off problems out of its depth correctly, or does it not realize it’s stuck? How does it map out its exact path toward decisions? What are the risks based on these paths? – Nick Heddy, Pax8
Resistance To Prompt Injection Attacks
Most agent production readiness assessments focus on capability: “Can the agent complete the task? If so, how accurately, how fast and how cheaply?” That’s the wrong axis. A perfectly capable agent can be steered into doing the wrong thing entirely by text it encounters in an email, a webpage, a support ticket, a file or a tool’s output. The question isn’t, “Can it do the task?” but, “Can it be made to do a different task by content it merely reads?” – Sanjay Dhawan, SymphonyAI
Cost Governance And ROI Tracking
The most overlooked factor is the cost and ROI of using the agentic workflow itself. Agents chain tool calls until they reach a good-enough answer, which is rarely the cheapest path there, and model drift can silently erode that ratio over time. Readiness means governing value, not just correctness: Measure cost per outcome, set controls that throttle or escalate when it breaks, and give every agent’s spend an owner. – Udam Dewaraja, StitcherAI Inc.
The Ability To Ask For Clarification
The overlooked factor is whether the agent knows when it’s wrong. Most models today confidently do the wrong thing without pausing to ask a clarifying question. That may look like decisiveness and speed, but in production it’s a liability. – Irina Bukatik, Branch
Identity Governance For Agent Access
One factor that’s often overlooked is identity governance. Before AI agents get production-level autonomy, enterprises need clear visibility into what each agent can access and when, what actions it can take, and how permissions are reviewed over time. Without that oversight, autonomy quickly expands risk rather than reducing operational complexity. – Paul Zolfaghari, Saviynt
Permission Limits For Agent Actions
Most readiness checklists focus on whether AI agents actually work, not what they’re authorized to do once they’re inside. Agents inherit credentials from service accounts or human users, which are often scoped far beyond a single task, granting them more access than required. The question organizations should be asking is, “What could this AI agent do that we didn’t ask it to do, and have we put controls in place to stop that?” – Art Gilliland, Delinea
Real-World User Adoption
Customer engagement and satisfaction are crucial. A ton of agents are getting deployed, but engagement on many of them stays low to nonexistent. Massive growth at Claude, ChatGPT and Gemini doesn’t mean every AI tool is growing. Smaller firms are shipping agents nobody uses. Skip that and you’re judging the agent on what it can do in testing, not whether anyone actually uses it. It might look ready, but then nobody touches it once it’s live. – Shayan Hamidi, Rechat
Data Coverage Across Decision Inputs
Data coverage gaps are important. It’s easy to overlook the gaps in data agents have access to, but such gaps can lead to overly ambitious or unrealistic judgments. Examples include an agent that suggests a Roth conversion without looking at the life situation—such as age or tax bracket—or a support agent that infers CSAT is high due to dropping call volumes but doesn’t know a major telecom system is down. To mitigate such risks, use a knowledge-graph-based decisioning framework to check for data sufficiency. – Sindhu Joseph, CogniCor
Workflow Retesting As Models Change
What teams easily miss is that the model they tested against won’t hold still. It keeps improving, and you’ll move to newer versions or even a different provider. The catch: The workflow you validated on one can behave differently on the next. I’ve seen an agent pass every test, then miss things after a change. Readiness isn’t a one-time pass. It’s rechecking the workflow each time it changes. – Lyle Pratt, Vida Global
Infrastructure Around The Agent
Assessing AI agents for production autonomy requires more than checking whether the model can reason through a task. The real test is whether the surrounding system is mature: tools, permissions, monitoring, recovery paths and failure limits. Without that detail, teams may misread brittle demos as readiness or blame the model for problems caused by weak infrastructure. It leads to noisy results. – Amit Ojha
Organizational Readiness For AI Autonomy
Many organizations are asking whether AI is production-ready. The better question may be whether your organization is ready. Tech is advancing at lightning speed, but that doesn’t mean organizations keep pace. AI agents don’t operate in a vacuum. Does your organization have people who know how to oversee it? Are teams trained? Are there accountability guidelines? Do employees know when to step in to manage agents? Without answers, companies may find out the hard way that organizational readiness is as important as the tech itself. – China Widener, Deloitte
Failure Behavior Under Pressure
It’s crucial to test, test and test again. It’s easy to create an MVP, but it’s really hard to make it production-ready. An agent that’s 95% accurate sounds production-ready until you realize the other 5% silently corrupts data or misleads users. Therefore, it’s vital to understand how an agent fails—does it halt, hallucinate or confidently take the wrong action—and to fix that prior to prime time. – Scott Burgess, Continu
Decision Auditability And Traceability
The overlooked factor is auditability of the decisions AI agents make. Governance regarding when, how and what decisions AI can make without human intervention, and then having traceability to those decisions, is critical. – Jason Kurtz, Basware
The Agent’s Knowledge Foundation
The most overlooked factor is the intelligence foundation beneath the agent. A capable model running on unstructured, stale or poorly connected knowledge will still produce confidently wrong answers. The agent isn’t the problem; what you’re feeding it is. – Sean Nathaniel, Upland Software
