Agentic AI isn’t just another wave of automation; it’s a structural shift in enterprise technology, one with the potential to completely redefine how work gets done. Previous waves of automation tackled parts of processes, leaving exceptions where humans had to step in. AI agents can reason, collaborate, and coordinate actions, allowing them to accomplish complex, multistep, nondeterministic processes that have so far depended on humans.

It’s easy to see the transformative potential of this, from improved operational efficiency and customer experience to sharper decision making and beyond. Forward-looking leaders aren’t asking if agentic AI will reshape their business but how to prepare their organizations to deploy it safely and effectively.

As executives reflect on how agentic AI might shape their business, from competitive positioning to their talent model, they will have to consider how it challenges the fundamentals of their IT architecture. Agentic AI architecture builds on the rise of composable microservices architecture and the use of enterprise cloud services that many companies have already been investing in. But to fully capture the value while navigating the risks, they will need to rethink how AI is embedded across the architecture—the systems, processes, and governance. Enterprises need to ensure agents have the context they need in real time, the ability to observe and explain behavior, and the guardrails to execute safely, securely, and cost-effectively. Current architectures simply cannot handle this balance when AI agents are used in the thousands across the enterprise—yet.

Architecture adapts to support agentic AI

Agentic AI should complement rather than take over the existing architecture. Tech teams will need to deploy it thoughtfully, with clear scope and controls in place. Agents are best for complex, nondeterministic problems that span multiple business domains and systems, rely on unstructured data and contextual reasoning, depend on real-time inputs, and until now have required human intervention.

Higher-level orchestrator agents are like project managers that oversee a whole process, breaking it down into subtasks and tracking progress. Task agents execute individual tasks and send back the results to the orchestrator. The orchestrator then compiles results and adjusts workflows as needed.  

Teams updating the IT architecture should consider several key principles as they evolve the design to support an agentic framework (see Figure 1).

Modernize the core platform. To fully realize the potential of agentic AI, many organizations will need to modernize their technology foundations. This means making core business capabilities easy for agents to find and use in real time. Achieving this may require reworking older, batch-based systems to be more flexible, accessible by APIs, and able to respond to real-time events. Adopting modular, industry-standard frameworks, like the Banking Industry Architecture Network, will help accelerate this shift. However, these modern systems will need to work alongside existing infrastructure for the foreseeable future, which could add some architectural complexity in the near term.

Ensure interoperability of agentic services. As agents roll out across the tech stack, consistent interoperability standards, such as the model context protocol (MCP), and frictionless integrations will be critical for breaking down silos and capturing the full value of agentic AI. Most organizations will need to support a mix of frameworks. These will include custom agents built on engineering tools, prebuilt agents embedded in vendor platforms, and dynamically generated agents in data platforms. Frameworks are themselves becoming more agentic. For example, a software development life cycle (SDLC) agent could coordinate a team of specialized agents (design, analyst, engineer, quality assurance) that collaborate to deliver a complete solution, from concept to deployment.

Distribute accountability. While central platform teams will control the core agentic platforms, accountability for assembling, training, testing, deploying, and monitoring agents needs to be distributed to business domains. Success will hinge on making domain expertise and knowledge assets—such as product documentation, business logic, feature stores, models, and data products—readily discoverable and accessible to agents.

Scale data access. Scalable access to structured and unstructured data is essential. Most organizations still lack the required ingestion pipelines for unstructured sources such as documents, emails, voice recordings, images, videos, and call transcripts. These data sources are critical for agentic reasoning, especially in manual or exception-driven processes where necessary knowledge often resides outside core systems of record or even outside the organization. For example, one European bank built foundational infrastructure to consistently use both structured and unstructured data to create a holistic view of each customer, enabling it to automate and personalize its engagement marketing—driving smarter, more targeted interactions at scale.

Update governance and controls. As agents take on more decision making, governance and controls must evolve. Real-time explainability, behavioral observability, and adaptive security are essential to mitigate risk, maintain customer trust, and avoid regulatory or reputational fallout. At the same time, organizations must manage the volatility of compute costs through dynamic resource allocation, edge deployment strategies, and AI-native financial operations practices.

Shift the engineering paradigm. Software engineering and DevOps processes, both tooling and workflows, need to evolve to manage the full life cycle of AI agents, including how they are tested, monitored, and safely deployed as they learn and adapt over time. AI agents are also poised to transform how engineering teams operate, taking on more of the day-to-day development, testing, deployment, and system operations. This shift will free up engineers to focus on higher-value work like architecture, strategy, and innovation.

Reimagine agent experience and access. In this new framework, agents become first-class channels and citizens. As channels, they are emerging as primary interfaces for interacting with customers and employees—on par with websites, mobile apps, and call centers. As citizens, they are fully embedded participants in business operations, empowered to act, make decisions, and collaborate across systems. This demands a reimagining of experience design: Conversational interfaces will dominate human engagement, while agent-to-agent coordination will drive autonomous action across workflows, systems, and even organizational boundaries. To make this work at scale—and safely—enterprises must establish robust frameworks for identity, consent, and fine-grained access control. For example, a South American bank that uses agents to facilitate real-time PIX payments through WhatsApp allows customers to simply send a photo or text describing the payment they want to make. The AI agent interprets the request, identifies the appropriate payment, confirms it with the customer, and then authorizes and sends the payment—all within a conversational experience.

The implementation imperative

Over the next three to five years, 5% to 10% of technology spending could be directed toward building foundational capabilities, including agent platforms, communication protocols, real-time data access and discoverability for agents, and modern security and observability frameworks. 

Over time, investment in agentic AI will grow. Up to half of technology spending could be on agents running across the enterprise to support business domains.

Still, the long-term economics are favorable, as efficiency and process improvements will outweigh the costs. Investments will need to be tightly focused, with an emphasis on delivering value quickly to ensure buy-in from the business. Frameworks for successful transformations typically follow a pattern of four motions:

• Focus on a few business domains to generate early value, rather than just building capabilities. Reimagining processes from end to end accelerates returns, lowers cost per agent, and lays the groundwork for scalable, enterprise-wide adoption.  
• Evaluate current architecture for agentic readiness, identifying the capabilities required to scale. This includes laying the groundwork for agent development toolchains, enabling seamless system interoperability, and fast-tracking the modernization of vector databases, event architectures, and core infrastructure.
• Define and embed observability, security, governance, and controls, providing traceability, accountability, anomaly detection, and cost discipline. For agentic AI to scale safely across the enterprise, these guardrails must be built in from the start, not bolted on later.
• Use agentic AI in the transformation to reduce effort, control costs, and ensure outcomes. Delivering value early helps fund the rest of an agentic transformation. 

Companies that don’t want to fall behind should be preparing and investing now: hiring and upskilling teams, embedding new capabilities, and understanding the necessary architectural changes. Those that wait will struggle to catch up. Agentic AI is already reshaping the enterprise, and only those that move decisively—redesigning their architecture, teams, and ways of working—will unlock its full value.