Generative AI has moved quickly from experimentation to enterprise adoption, but the next stage is significantly harder: enabling AI agents to reason, invoke APIs, interact with enterprise systems, coordinate workflows, and take action within complex business processes.

Traditional enterprise architectures were not designed for autonomous software actors.

This session presents a practical, vendor-neutral framework for moving from isolated Generative AI pilots toward governed, observable, and production-ready agentic AI.

Drawing on lessons from mission-critical financial-services architecture, large-scale enterprise modernization, and applied work in agentic systems, the session introduces the concept of an Enterprise AI Control Plane: an architectural layer that connects models and agents with the enterprise controls required for secure and reliable operation.

Attendees will explore practical implementation patterns for integrating AI agents with existing APIs, microservices, event streams, data platforms, and legacy systems while preserving the architectural discipline required in regulated and high-availability environments.

The session will examine how organizations can:

Establish agent identity, delegated authority, policy enforcement, and least-privilege access.
Introduce human approval and escalation controls for high-risk or irreversible actions.
Implement observability across prompts, tool calls, workflows, decisions, and autonomous actions.
Evaluate agent behavior, reliability, and operational risk before production deployment.
Maintain semantic consistency when agents interact with fragmented enterprise systems and data.
Design resilient workflows that account for model failures, API failures, downstream outages, and uncertain agent behavior.
Integrate Generative and Agentic AI incrementally without requiring wholesale replacement of existing enterprise platforms.
Create clear governance boundaries between AI reasoning and enterprise execution.

A practical reference architecture will illustrate how models, agents, enterprise services, APIs, identity, policy, observability, security, data, and human governance can operate as a coordinated system.

The session will also address one of the most important architectural shifts created by agentic AI: enterprise APIs are no longer consumed only by applications and people. Increasingly, they will be consumed by autonomous agents acting on behalf of users, systems, and business processes. This changes how organizations must think about authentication, authorization, traceability, lifecycle management, operational resilience, and risk.

Rather than presenting another AI platform or model comparison, this session focuses on the architectural and operational capabilities enterprises need regardless of which models, clouds, or AI platforms they adopt.

Attendees will leave with three practical outcomes:

A reference architecture for connecting AI agents safely to enterprise systems, APIs, data, and workflows.
A governance model for agent identity, authority, observability, human oversight, and operational risk.
An incremental modernization approach for introducing agentic capabilities into existing enterprise environments without disrupting mission-critical systems.

The objective is to help technology leaders move beyond the question of “How do we use Generative AI?” toward the more important enterprise question:

“How do we allow intelligent systems to act within the enterprise while keeping those actions secure, governed, observable, resilient, and accountable?”

The session provides attendees with a practical architecture and decision framework they can apply when moving from AI experimentation toward production-scale Agentic AI.