A Practical Guide to AI Agents in the Enterprise
AI agents are moving beyond demos into real enterprise use: reviewing code, triaging incidents, automating workflows, and interacting with customers. However, most teams focus on the LLM and overlook what actually makes agents work in production.
This lab covers the core building blocks of production-ready agents:
- What defines an agent, including the reasoning–action–observation loop
- Tool and integration design, and why it often matters more than model choice
- The role of memory architectures in complex, multi-step tasks
- Planning and self-correction patterns for reliable execution
- Evaluating quality, reliability, and safety in non-deterministic systems
- Agent security, including risks of autonomous actions, access to sensitive systems, and how to get this right from the start
By the end of this lab, you’ll understand what actually makes AI agents work in enterprise production, beyond just choosing the right model.
Speakers
Nadheesh Jihan is an AI leader at WSO2 with 8+years of experience applying AI across IAM, API Management, and enterprise integration. He brings a multidisciplinary perspective, combining deep systems expertise with a strong understanding of how AI and agentic architectures fit into real-world enterprise environments. He is a core contributor to the Agent Manager initiative, shaping how agentic AI systems are built and governed at scale. His work focuses on applying AI to enterprise platforms while also leveraging those platforms to enable scalable, production-grade AI systems.
Chintana's role is helping customers to figure out how to architect modern cloud-based applications and move their existing workloads to the cloud. Rethink and integrate all aspects of application architecture from domain driven design, data architecture, authentication/authorization, and API/integration services. He has over 17 years of experience working large scale enterprises in different industry verticals such as aerospace and finance.