Architecting Enterprise AI: From LLM Integration to Secure AI Agents
AI proofs of concept are easy to start. Making AI work across an enterprise, securely and consistently, is an architecture challenge.
As organizations adopt large language models (LLMs), architects must decide who can consume models, how access is governed, where identity and security boundaries belong, and whether AI capabilities should be shared centrally or managed by individual teams. These decisions become more consequential when AI applications invoke tools and evolve into agents.
Architecting Enterprise AI is a live, two-hour virtual architecture masterclass from Packt, led by Stéphane Eyskens. Through design diagrams, technical demonstrations and practical trade-off discussions, the session examines centralized versus decentralized LLM consumption, enterprise identity and guardrails, internal versus internet-facing Model Context Protocol (MCP) architectures, and the evolution from LLM API calls to AI agents.
The focus is enterprise architecture rather than model training or a code-along lab: how to choose integration patterns, define trust boundaries and design AI capabilities that organizations can operate and govern.
Key takeaways
By the end of the session, participants will be able to:
Explain why enterprise AI architecture extends beyond data science and model selection.
Compare centralized and decentralized LLM consumption patterns and their trade-offs.
Identify identity, authorization, security and guardrail controls for enterprise AI integrations.
Distinguish internal and internet-facing MCP architecture patterns and their security implications.
Describe how architectural requirements change when LLM applications become tool-using agents.
Frame enterprise AI architecture decisions around secure access, governance and operational ownership.
Who should attend
Enterprise architects, solution architects and cloud architects
AI architects, cloud-native architects and technical leads
Platform engineers and teams integrating LLMs into enterprise systems
Security and identity architects responsible for AI governance
Engineering managers and technical decision-makers planning enterprise AI adoption
Level: Intermediate to advanced. Familiarity with cloud architecture, APIs and basic LLM concepts is helpful. No previous MCP or agent-building experience is required.
Topics covered: Enterprise AI architecture; LLM consumption patterns; LLM gateways; identity and access management; AI security and guardrails; Model Context Protocol (MCP); enterprise AI agents; AI governance.