Flagship enterprise AI course
Enterprise Agentic AI Engineering using Azure OpenAI, MCP and OpenAI Agents SDK
Help your senior developers, architects and AI teams move beyond chatbots into secure enterprise agents that call tools, use business data, request approvals, emit traces and operate with measurable guardrails.
Agents are the difference between software that answers questions and software that does work — and the leap brings a new class of engineering responsibility. An agent that calls tools and acts on business data needs explicit decisions about autonomy, tool access, approvals, memory and observability that chatbots never required. The organisations that succeed will not treat agents as a prompting exercise: they will engineer them with the same discipline as any production system, using the SDKs, protocols and guardrails purpose-built for the job.
This three-day, hands-on course takes senior developers, architects and AI teams from agentic foundations — agents vs chatbots, planning, tool use, memory, autonomy boundaries and workflow mapping — to a working enterprise agent built on Azure OpenAI, the OpenAI Agents SDK and MCP. You learn model selection with structured outputs and grounded prompts, then orchestration: agent definitions, handoffs, approvals, tracing and multi-step workflows. MCP is covered as an enterprise integration protocol — clients, servers, tools, resources, prompts and the security boundaries that make tool exposure safe — followed by agentic RAG that grounds agents in your knowledge with retrieval, memory and citations. The course finishes on the hard part: prompt injection and tool poisoning, authorization and auditability, agent evaluation, and the latency and cost controls that make agents operational.
You leave with a working agent you built during the course — one that calls real tools, respects approvals and emits traces — along with the architecture patterns, evaluation harness and security checklists to scale it safely. The course is aimed at senior developers, architects and AI engineering teams in enterprises that are already using Azure OpenAI and want to move from chatbots to autonomous workflows with confidence. By the final day you will be able to decide where autonomy is appropriate, how to expose tools through MCP safely, what to trace and measure, and how to run an agent programme with guardrails your security team will accept.
What Your Team Will Be Able To Do
The course is built for implementation teams that need practical decisions, not generic AI awareness. Every module connects agent design to data access, control, evaluation and production readiness.
Design agent workflows
Translate business processes into agent workflows with tools, memory, approvals and human oversight where risk demands it.
Build with enterprise tools
Use Azure OpenAI, tool calling, MCP concepts, OpenAI Agents SDK patterns and enterprise data retrieval safely.
Operate with controls
Add tracing, evaluation, cost controls, guardrails, access boundaries and release review practices before rollout.
Course Modules
Agentic AI foundations
Agents vs chatbots, planning, tool use, memory, autonomy boundaries and workflow mapping.
Azure OpenAI and model choices
Model selection, structured outputs, grounded prompts, tool calling and enterprise usage patterns.
OpenAI Agents SDK
Agent definitions, orchestration, handoffs, approvals, tracing concepts and multi-step workflows.
MCP for enterprise integration
MCP clients and servers, tools, resources, prompts, security boundaries and integration decisions.
Agentic RAG
Retrieval, search tools, memory, citations, validation and enterprise knowledge grounding.
Security, evaluation and operations
Prompt injection, tool poisoning, authorization, auditability, agent evaluation, latency and cost.
Hands-On Capstone
Participants build an enterprise agent design that retrieves internal knowledge, calls a business API or tool, asks for approval when required, returns traceable output and includes security and evaluation checklists.
Deliverables included
- Reference architecture for agentic enterprise systems
- Agent workflow design template
- Lab code outline and tool integration pattern
- Security checklist for agent and MCP workflows
- Evaluation checklist for quality, cost and risk
Bring This Course To Your Engineering Team
Share your target audience, cloud stack, preferred dates and whether you want labs tailored around Azure OpenAI, Azure AI Foundry, internal APIs, MCP servers or agent security review.