From Software to Systems of Intelligence: Why AI Agents Are Redefining Enterprise Control, According to Divyesh Patel
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When Divyesh Patel founded Radixweb in 2000, his ambition was straightforward: building the best software company he could. Over the next 25 years, he delivered technology solutions to more than 3,500 clients across every major industry, watching the cloud reshape infrastructure, mobile reshape user experience, and data reshape competitive strategy.
"Every transformation, including cloud, mobile, and big data, changed what software does," says Divyesh Patel, CEO of Radixweb. "Artificial intelligence agents change what software is. It has consequences that most enterprise leaders have not yet thought through."
The Contract That Made Enterprise Technology Governable
Enterprise software operated under an implicit agreement between software and the humans who ran them. If something went wrong, the chain of responsibility was traceable.
"The entire organizational architecture: governance frameworks, audit methodologies, compliance structures, everything was built around it," he says.
AI agents are goal-directed solutions. They determine the sequence of actions themselves. They call APIs, read and write data across systems, delegate subtasks to specialized sub-centres, retry when something fails, and reroute when a path is blocked.
Not Automation. Something Categorically Different.
AUTOMATION removes human effort from a defined task. The boundaries of what systems can do are pre-programmed.
Autonomous agents are given intent, and they determine the path themselves. The boundary of what they can do is a soft constraint defined by the tools and permissions they have access to. They adapt, retry, and route around obstacles.
In traditional automation, control is exercised at the point of execution. For the latter, by the time a human is reviewing an outcome, a hundred micro-decisions have already been made.
Four Places the Old Frameworks Break First
Patel identifies four critical control points here:
- AUDITABILITY – Traditional logging captures the path of an agent, not why it chose that route. If the decision-making isn’t clearly auditable, businesses will face tremendous exposure.
- ACCOUNTABILITY - When outcomes drift from intent, the chain of responsibility is not clear. Systems must have defined accountability metrics before deployment.
- DATA SOVEREIGNTY - Information an agent can reach is frequently broader than what it should be touching for any given task.
- CHANGE MANAGEMENT - Employees working alongside intelligent set-ups are being asked to supervise systems that have their own initiative. Most organizations do not yet have the vocabulary, the workflows, or the training infrastructure to support it.
Control Does Not Disappear. It Moves Upstream.
The argument Patel makes most forcefully, is not a warning against agentic artificial intelligence.
"Control does not disappear," he says. "It moves upstream. And that is a more demanding form of governance."
In a traditional software environment, the control mechanism is woven into the workflow, visible and auditable. In an agentic environment, by the time execution has happened, the consequential decisions have already been made. The control points are in how objectives are defined, what permissions are granted, where human interruption is required, and how the reasoning behind actions is observed.
Patel identifies four governance levers that enterprises must build:
GOAL GOVERNANCE - Define objectives of autonomous setups with enough precision. "The clarity of your intent is the first line of your compliance framework," he says.
BOUNDARY ARCHITECTURE - Determine which systems, data sources, and action types are in scope. It is a capability contract that defines what the agent is for as much as what it cannot do.
INTERRUPT DESIGN – Identify the decision thresholds at which a human must be brought into the loop.
OUTCOME OBSERVABILITY - Move beyond logging what an agent did to understanding why it made the choices it made.
A Leadership Question, Not A Technology Question
"Organizations treat AI agents as a technology procurement decision," he says. "They rather are a governance and organizational design decision that have a technology component."
These questions must be answered at the board level, Patel argues:
- What kinds of decisions are you comfortable delegating to agentic AI that acts on its own?
- How is success defined when autonomous setups are optimizing for goals specified imprecisely?
- What is the accountability model when an agent produces unintended outcomes?
- What new literacy does senior leadership need to interrogate the decisions by AI agents?
The Right to Trust
"Systems of intelligence are becoming something closer to colleagues, ones that do not ask permission, do not get tired, and do not always explain their reasoning. The leaders need to build the governance architecture to earn the right to trust them."
The question he leaves with enterprise leaders is not the one most of them are currently asking. What should they be allowed to decide?
Disclaimer: No Business Standard Journalist was involved in creation of this content
Topics : AI Models
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First Published: Apr 01 2026 | 1:20 PM IST