By Deepak Visweswaraiah, SVP and MD, Pegasystems India
Agentic AI, in my opinion, has also started on the trajectory that AI did when it was first introduced. It has become overhyped without considering its real process and the results that it can bring when deployed intentionally. Most enterprises today have deployed agentic AI across their operations, they have proof of concepts, running pilots, and agents performing tasks. However, even with multiple agents deployed, the result has been underwhelming. The work largely remains disconnected coupled with rising costs, inflated risk, inefficiency, and frustration. Why? Silos.
There is no intention, no governance, no workflow decisions, and no KPIs. Industry reports have also confirmed that most organisations are still in the experimentation or piloting phase, and nearly two-thirds of organisations have not even begun scaling AI across the enterprise. The underlining factor here is that this gap is not a technology problem, but a design problem.
The AI pilot trap
Enterprises have gotten good at experimenting with AI. A team starts with a problem, builds a workflow or deploys agents around it, and it works. Initially, these pilots run well and deliver results, however, when it comes to scaling the operation, the project stalls. The concerning factor here is that most successful pilot programs are not scalable. This is because they are built on systems that weren’t intended to interact with each other using agentic AI.
These systems operating in silos create an environment of disconnected automation, opaque decision making, and isolated agents acting independently of intent. Execution occurs within one system while decisions occur in another. Even if the AI agents are very functional on their own, they will end up operating within a silo with little to no insight or visibility surrounding them. In such cases, we are left with ineffective or non-meaningful results, and the intended operational transformation doesn’t happen.
What agentic operations require
An agentic system does more than just execute instructions. It takes inputs, analyses them, determines an action, evaluates the outcome against the desired result, and then adjusts accordingly. This closed loop must operate continuously across the entire value chain. For that to work, three foundational elements must come together. First, AI must have access to accurate and real-time data. Second, there must be clear boundaries defining what AI can decide autonomously and where human judgment is required. Third, governance must be embedded directly into the workflow and not layered on as a post-process review (being AI native in a platform).
Next comes design. AI must understand not only how to perform an activity, but what it is ultimately trying to achieve. That clarity of intent allows it to refine decisions in pursuit of measurable results. This begins with designing processes, data, and decision logic as a unified architecture rather than as separate components. When workflows and objectives are aligned from the start, AI can be embedded at the right points, enabling coordinated and end-to-end orchestration. AI that is bolted on may not be able to do this well.
Considerably, none of this is possible on a fragmented foundation. Most enterprises still operate on processes shaped by legacy system limitations. Over time, teams built separate workflows, data stores, and decision logic that hardened into operational silos. When AI is deployed on top of this, it does not dissolve the silos but worsens it. Fragmented automation results in chaos, creating an illusion of efficiency while it quietly adds more complexity.
Embedded decisioning, governance, and responsible AI
In many organisations, decision-making still occurs outside the flow of work. Data is extracted, analysed, and then fed back into operations. This process delays and creates opacity in the entire workflow. Agentic operations require decisioning to be embedded directly within workflows so that the intelligent systems can guide execution in real time. This integration closes the gap between insight and action. Governance follows the same principle; when it’s treated as a final checkpoint, it often slows progress. When built into the system through defined boundaries, traceable decision histories, and clear human oversight, it accelerates both speed and confidence.
Furthermore, when deploying AI agents, responsible AI measures are indispensable. Knowing what decisions were made, why they were made, and within what constraints, helps in optimally scaling the responsible AI practices. These guardrails are not barriers to innovation, but the foundation of trust which ultimately enables AI to scale safely and meaningfully across the enterprise.
The future is intentional agentic operations
The next chapter of enterprise AI will not be defined by how many agents an organisation deploys, but by how intentionally those agents are designed to work together. The future belongs to enterprises that move beyond experimentation and towards systems built with clear intent at their core. The good news is that the technology to enable this shift is already here. What comes next is a design and leadership imperative.
Organisations must deliberately architect how intelligence flows across the enterprise, ensuring AI is not another layer of complexity, but a force that simplifies, connects, and strengthens operations. The real transformation will come from intentional systems that turn AI from a collection of tools into a coordinated and trusted operational capability that can further be scaled under any circumstance. What enterprises need today is intentional agentic operations that work at scale, without someone having to manually stitch all the pieces together. Mastering this, will enhance the real future of agentic enterprise.