Agentic systems have moved beyond recommendation. They now initiate actions: routing cases, triggering communications, escalating complaints, and shaping customer outcomes in real time. Enterprises have embraced this shift for its speed and scale, but a structural weakness has emerged: decisions now travel faster than the systems designed to authorize, audit, and reverse them.
The risk is not whether AI works. It is whether organizations can control what happens after it does. As autonomous behavior spreads across enterprise workflows, governance can no longer live in policy documents or pre-deployment reviews. It must exist inside production systems, where decisions turn into customer impact.
The risk is not whether AI works, but whether organizations can control what happens after it does. As Ronith Pingili explains in “AI’s Dirty Windshield Problem,” models alone do not make AI trustworthy. What matters are real-time data and guardrails that keep actions grounded and safe. Governance can no longer live in policy documents or pre-deployment reviews, it must exist inside production systems, where decisions directly impact customers.
Ronith Pingili, author of “The AI-Fueled Illusion: Why Smarter Applications Demand Tougher Infrastructure,” has encountered this gap not as a theoretical concern, but as an operational fault line. Working deep inside enterprise CRM systems that sit directly between automation and end users, he has seen how quickly intelligent workflows can outrun the guardrails meant to contain them. His work has focused on embedding authority directly into execution paths: designing systems that allow teams to scope behavior, pause it selectively, and reverse it without destabilizing everything around it.
“Once AI starts taking action inside customer workflows, governance stops being a policy question and becomes an engineering one,” Pingili says. “If you cannot control behavior after it ships, you are not running automation; you are accepting risk.”
This tension between autonomous speed and accountable control now defines the next phase of enterprise AI. And it is precisely at this seam, where decisions turn into outcomes, that the agentic enterprise remains most exposed.
Autonomous systems excel at identifying patterns and initiating responses. What they do not manage is liability. Once an AI-driven action reaches a customer, whether through a support decision, a compliance flag, or an automated response, the organization owns the outcome.
This is the under-discussed gap in the agentic enterprise. Most architectures still assume that control happens before deployment, not during execution. Yet by 2026, 40% of enterprise applications are expected to embed autonomous or semi-autonomous actions, accelerating the pace at which decisions materialize inside customer workflows.
When something goes wrong, the failure is rarely technical. It is structural. Enterprises struggle to answer basic questions in real time: Who authorized this behavior? Which users were affected? How fast can it be stopped without collateral damage?
As a judge at the Stevie Awards for Sales and Customer Service, he evaluates operational excellence at scale, and the same pattern appears repeatedly: breakdowns are not caused by intelligence gaps, but by missing accountability paths once automation crosses into execution.
“AI does not carry liability, workflows do. Once an automated decision is committed inside an operational system, accountability shifts from the model to the architecture that allows it to execute unchecked.”
The promise of agentic systems is efficiency. The danger is invisibility. As AI behavior becomes embedded in everyday workflows, organizations often discover problems only after customer impact has already occurred.
Traditional rollback mechanisms are too blunt for this new reality. Full production rollbacks take hours, disrupt unrelated functionality, and obscure root cause analysis. In regulated environments, the cost of these delays is significant. Industry studies now estimate that uncontrolled automation failures in regulated systems can exceed $4 million per incident, once remediation, downtime, and compliance exposure are counted.
“The most dangerous failures in agentic systems are not outages; they are unbounded behaviors. When automation lacks scope control, small decisions propagate quietly until intervention becomes expensive, public, or irreversible.”
This is where governance breaks down, not because teams lack intent, but because they lack tooling designed for partial exposure, instant reversibility, and auditable control once systems are live.
This challenge became tangible for Ronith while working on customer-facing automation inside Salesforce at Block. As automation and AI-assisted workflows expanded, teams needed a way to manage behavior after deployment.
“In an agentic system, deployment cannot be the point of finality,” Pingil, a 2026 IEEE SusTech conference reviewer, says. “Behavior must remain governable at runtime; measurable, reversible, and scoped. Otherwise autonomy becomes indistinguishable from loss of control.”
Ronith led the design of a feature-flag governance framework integrated with LaunchDarkly that treated AI-driven behavior as something to be actively controlled at runtime. Instead of deploying features in an all-or-nothing state, the system enabled percentage-based exposure, instant toggling, and scoped activation across user cohorts. Crucially, this control operated independently of code releases.
The impact was structural. Feature rollback time dropped by more than 95%, from hours to minutes. Annual savings exceeded $100,000 by avoiding full production rollbacks and minimizing disruption. More importantly, the organization gained the ability to pause, reverse, and audit behavior without breaking surrounding workflows.
This approach reflects a broader pattern emerging across enterprises. Research shows that organizations implementing controlled rollout and observability mechanisms reduce automation-related incidents by 30 to 40% compared to unmanaged deployments. Governance, when designed as infrastructure, becomes a stabilizing force rather than a bottleneck.
Agentic systems do not fail loudly. They fail quietly: by acting correctly in the wrong context, at the wrong time, for the wrong users. When that happens, the question is not whether AI made a mistake, but whether the organization can intervene fast enough to contain it. Even sophisticated models break down when accuracy, consistency, and timeliness are not enforced as system-level guarantees, long before any decision reaches an end user.
“The enterprises that succeed with AI will not be the ones that automate the fastest,” Pingili concludes. “They will be the ones that preserve the ability to intervene; technically, immediately, and without collateral damage when autonomy collides with reality.”
The next phase of enterprise AI will not be defined by smarter models alone. It will be defined by systems that bind autonomous decisions to authorization, reversibility, and accountability. Governance is no longer a compliance afterthought. It is the infrastructure that determines whether AI accelerates trust or erodes it.
The agentic enterprise does not need less automation. It needs stronger control where automation meets reality.