How to get your enterprise architecture ready for agentic AI

Agentic AI works in a different way than the gen AI tools many enterprises have deployed over the past two years. Where ChatGPT-style assistants analyze data and recommend actions for humans to execute, agentic AI systems take multiple autonomous actions to achieve business goals. These systems access data across the front, middle, and back office, as well as to external sources. They break down historic functional silos to automate broader end-to-end value chains, without human intervention.

According to Accenture’s latest Pulse of Change survey of 3,650 executives conducted late last year, 67% of executives said AI would substantially or completely transform their organization’s core. Some 15% said they’re working on agentic POCs, with 31% running pilots in specific functions, and 31% currently deploying agents across multiple functions.

This suggests that while agentic AI is a workplace reality, most organizations are at the start of their journey. To get beyond POCs and limited pilots, companies will need to adjust their infrastructure to support autonomous operations across system boundaries.

3 critical upgrades and how to execute them

1. Data quality requires new standards and guardrails
When an autonomous agent makes workflow changes based on flawed data, errors can propagate through systems before anyone notices. The solution is to implement robust auditability and observability systems, including human-in-the-loop processes.

Action: Refresh customer, product, or financial data quality standards in one critical domain in your business. Implement automated quality monitoring that flags anomalies before they affect agent operations. Work also with the business on a data-quality governance model, and test and validate this governance process in one domain before expanding to others.

2. Make your integration architecture agent friendly
Traditional interfaces such as APIs move data between systems. However, they lack the full context agents need to understand, access, and orchestrate data across your business functions. This requires agent-friendly interfaces that go beyond simple data exchange.

Action: Focus your integration investments on the systems that agents will orchestrate across most often. Working with clients, the ones we see most often include customer-facing operations, financial workflows, HR platforms, IT infrastructure, and compliance. But create agent-friendly APIs for these core paths first. Start with event-driven architectures that reveal not just data, but business context and rules that agents can understand autonomously. For time-critical value chains, consider deploying real-time data integration and event-based architecture patterns.

3. Build a new architectural layer to enable autonomous operations
Organizations need to create an agent tier in their enterprise architecture. This is made up of:

• Cognitive AI that provides reasoning capabilities.
• Autonomous orchestration systems that trigger appropriate applications.
• Agent lifecycle management, which onboards, updates, and monitors AI agents.
• A semantic spine. This is additional content that allows AI agents to interpret, relate, and reason over different data silos in a business, such as agent-friendly APIs.

The good news is this agent tier is built onto your existing infrastructure as an enhancement rather than a replacement.

Action: Dedicate a cross-functional team to design and implement an agent tier for one high-value use case. Focus on customer-facing or revenue-generating processes that’ll capture the attention of your C-suite peers. Accept that the initial implementation won’t be architecturally perfect. The goal is to show the potential value while identifying what scaled deployment will require.

180 days to create a first end-to-end agentic process

At large companies, typical enterprise architecture initiatives would unfold over 18 to 24 months. However, agentic AI is collapsing these timelines as the potential business value is justifying the urgency. According to our latest Pulse survey, 21% of organizations are redesigning end-to-end processes with AI at the core. A further 45% are converging multiple processes using AI.

So tech leaders who want to lead should focus on ambitious milestones: 60 days to define a strategy, 90 to build an agent tier, and 180 to deliver the first end-to-end application. This approach builds architectural maturity through rapid implementation rather than extended planning.

Days 1-60: Strategy and assessment. Run an enterprise architecture maturity assessment — typically in four to eight weeks — to find specific problems that’ll stop agents from working at scale. Simultaneously, evaluate which processes are customer-facing, revenue-generating, and feasible to implement within 180 days. You can also use this process to assess where proprietary agent development could create a defensible competitive advantage.

Days 61-90: Foundation building. Establish the agent tier for your selected use case. Upgrade data governance and API capabilities for the specific systems this use case requires. Implement auditability and observability mechanisms, and create the semantic spine that enables agents to interpret data from the involved systems.

Days 91-180: Deploy and validate. Launch your first end-to-end agentic application. Moving forward, monitor performance closely, focusing on where autonomous decision-making works well and where human oversight remains necessary. Use this real-world implementation to validate your architectural choices and identify what needs strengthening before scaling deployment across your business.

The great beyond: scale strategically

With one successful implementation, conduct diagnostics of your digital core within 12 weeks to plan the broader rollout of your agentic workflows. Using what you’ve learned, build custom agents for strategic, unique processes that are rapidly evolving. Balance these resource-intensive projects by deploying off-the-shelf platforms for more standardized functions, such as back-office operations. With the wealth of experience from your 180 days, map out your integration priorities and the governance frameworks for your expanded use cases

The enterprise architecture gap separating current capabilities from agentic AI requirements is substantial. But organizations that establish a foundational agentic architecture within 180 days, even if imperfect, will position themselves to capture value while their competitors remain in pilot mode.

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