Research
Tech Navigator:
Foreword
Rafee Tarafdar
Executive vice president and
chief technology officer, Infosys
As agentic AI moves from scattered pilots to full-scale production, demonstrating consistent value and resilience is emerging as the key challenge.
The bottleneck is not a shortage of enterprise imagination, but a gap in architectural readiness. Most enterprises do not yet have real-time data fabric, orchestration patterns, and governance frameworks, and they struggle to design the information environment that agentic systems demand in 2026.
This gap remains poorly understood, particularly in terms of what coordinated multiagent systems mean for enterprise architecture, business processes, and human accountability.
Yet the foundations have already been laid. A decade of cloud migration, API-first design, and agile transformation has built the scaffolding needed to progress. The challenge is that this scaffolding was designed for human-initiated workflows, so significant changes are needed for a landscape where autonomous, agent-driven software carries out critical business processes.
Hence, what’s really needed is adaptation that takes into account the specific demands of autonomy while ensuring that the design thinking approach taken always keeps humans in the loop.
In our implementations, a deliberate upfront focus on the following four areas has proven to be the key to success:
- Data and context engineering: Getting the right data in the right context is critical to agent performance. Organizations should design retrieval pipelines, structured workflows that prepare relevant knowledge for an agent before it reasons or acts, along with memory management and prompt architecture. This ensures agents act on precisely the right information, and not on raw documents or stale prompts.
- Ecosystem orchestration: Production systems involve specialized agents, with capabilities that include planning, executing, and verifying outcomes. These agents must coordinate through defined patterns and interface with enterprise, data, and business platforms. These interfaces must be enhanced with semantic richness — a term used to describe the amount of meaningful information associated with a concept or word, predictable error handling, and clearly bounded capabilities.
- Agentic experience: Customers will no longer tolerate stiff, robotic interactions branded as intelligence. Agent experiences must be designed with the same rigor as your best digital products, ensuring that intelligence and actions are embedded in the flow of user activities to deliver a seamless, experience.
- Engineered guardrails: Governance must be integral to runtime architecture. Input validation, output filtering, tool-use constraints, cost ceilings, and fallback behaviors must be built into the agent’s execution layer before deployment. Equally, escalation paths, decision boundaries, and audit trails should be in place before agents go live, not after.
Underpinning all four areas is agentic observability. In 2026, traditional monitoring won’t suffice, and without agentic observability, trust cannot scale. A focus on implementing trace-level visibility into reasoning chains, tool calls, and decision points will make all the difference to AI transformation journeys. The path to agentic AI doesn’t require starting again. It requires evolving intentionally across these four dimensions. The enterprises that move fastest and most successfully will be those where IT and business build shared agentic fluency, and do so quickly. This must be done with a focus on making humans the final arbiter of what is allowed to happen in the enterprise.
This edition of Tech Navigator provides a pragmatic, actionable, and in-depth blueprint for architecting the agentic-first enterprise. We hope it helps you move forward quickly to achieve lasting business success.
Chapters
The success of agentic AI doesn’t lie in the power of the model but in the quality of context provided to it. Here, we introduce an agentic maturity model to help engineer your organization’s data stack for the new era.
Agentic AI promises an enterprise that learns and evolves. To get there, process and integration architecture must evolve to acquire semantic sensing and intelligence characteristics built on the right data architecture.
The agentic enterprise requires a new interface, one where humans guide and orchestrate workflows with all information at hand, safe in the knowledge that any exceptions will be handled by AI agents working in the background.
Integration tools have evolved from simple custom connections between systems to shared platforms that help services communicate reliably. New tools are emerging to support agent-driven systems, including agentic AI gateways to manage and coordinate AI agents.
Agentic AI adoption is increasing. But traditional security isn’t adequate for the agentic age. Organizations should focus on building an operating system that prescribes what agents can do, and what’s at risk if they don’t.
