Chief Technology Officer
Onix
Enterprise AI is entering a new phase. The earlier wave of tools automated predictable tasks and assisted with decisions based on static models. But agentic AI is something far more dynamic. These context-aware AI agents can work independently, pursue goals, and adjust their behavior based on changing inputs. They are capable of initiating actions rather than simply responding to commands, which makes them useful in complex business environments.
Considering these advantages, over 80% of Indian organizations are now exploring the development of autonomous agents. However, deployment of these agents can be a challenge with traditional enterprise architecture, which was never built to support such autonomous systems. It means that the existing technology stack needs to be redesigned.
Building Scalable, Composable AI Pipelines
Earlier, scalability used to mean increasing server capacity or expanding cloud storage. But in the context of agentic AI, scalability means being able to manage operations across departments with the help of agentic AI. These autonomous agents need continuous access to structured and unstructured data. They also need clear rules on what they can and cannot do and a secure way to interact with both modern APIs and older systems still in use.
To make this work, organizations must move toward a composable model that facilitates the data-to-AI pipeline, automating the entire lifecycle from data ingestion to actionable AI insights. Information should also be structured in a way that allows agents to retrieve exactly what they need in real time without excessive computation or delays.
Embedding Continuous Security and Governance
In the past, human error used to be the primary point of failure. Now, with agentic AI, threats could come from faulty logic, compromised data, or external actors attempting to manipulate behavior. Therefore, modern enterprise architecture must adopt robust governance, observability, and security as core pillars supporting scalability.
This means designing systems with built-in traceability, rigorous access controls, and continuous real-time monitoring of KPIs and data drift. Furthermore, implementing zero-trust models ensures that every decision is verified in context. All actions should be logged, not just for compliance but for post-event learning. It is not just about stopping breaches but understanding what led to them.
Ensuring Transparency and Explainability
Autonomous agents that operate in regulated sectors must not only work correctly but also be able to explain how they reached a conclusion. In that context, knowledge graphs and data lineage play a pivotal role. They enhance explainability and data visibility, providing clear, readable trails showing how decisions are made. For instance, if an agent rejects a loan application or reroutes inventory, the underlying reasoning should be transparent and accessible without requiring decoding of black box models.
Integrating Human Oversight and Control
AI agents are not here to replace human workers. They are here to handle the volume, repetition, and machine-speed reactions. People still define strategy, set goals, and manage relationships. For agentic AI to succeed, humans must remain part of the process. That means the architecture must include tools for humans to supervise, adjust, and occasionally override agentic decisions.
Enabling this kind of partnership requires thoughtful design. The interface must allow users to interpret, guide, and redirect agent behavior with ease. The training data itself must reflect the complexity of real-world scenarios to reduce unexpected outcomes. In that scenario, solutions like synthetic data generation can help build diverse, representative datasets that improve how agents perform under varying conditions. By combining machine efficiency with human judgment, organizations can unlock superior outcomes.
Building for What Comes Next
Agentic AI is not a passing phase. It is a structural shift in how digital systems work. As adoption increases, so will expectations. Enterprise architecture must evolve to handle not just smarter tools but smarter systems that learn, adapt, and act independently. Tasks such as code transformation and validation are increasingly automated, becoming integral to an evolving enterprise AI lifecycle.
This will require investment in addition to a cultural shift in how teams approach data, automation, and decision-making. But for those willing to make the change, the payoff is clear. Businesses will be better equipped to respond to change, scale operations, and unlock new value, driven by intelligence that grows alongside them.
Authored by Niraj Kumar, Chief Technology Officer at Onix