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AI Agent Builder: Practical Enterprise AI Capability Building
An advanced, hands-on program for professionals who want to design, build, integrate, test, deploy and manage production-ready AI agents. It covers the full AI Agent Development Lifecycle, from architecture and workflow design to RAG, MCP, tool calling, memory, multi-agent orchestration, AgentOps, security, evaluation and enterprise deployment.
Six capabilities this program builds so you can ship production-ready AI agents.
Design enterprise AI agent architectures using modern reasoning and planning patterns.
Implement advanced prompt and context engineering, including memory and RAG.
Connect enterprise systems and tools to agents using MCP and tool calling.
Develop multi-agent workflows and orchestration for complex business processes.
Secure AI agents with enterprise guardrails and evaluate their quality and performance.
Deploy and monitor production AI agents using AgentOps practices.
How the program moves you from design fundamentals to a deployed, working agent platform.
Learn AI agent engineering concepts and production architecture.
Build agents using leading frameworks and design patterns.
Connect enterprise applications, APIs and MCP servers to your agents.
Design collaborative agent systems that coordinate complex tasks.
Deploy, monitor, evaluate and observe agents in production.
Build, deploy and present a production-ready enterprise AI agent platform.
Learn AI agent engineering concepts and production architecture.
Build agents using leading frameworks and design patterns.
Connect enterprise applications, APIs and MCP servers to your agents.
Design collaborative agent systems that coordinate complex tasks.
Deploy, monitor, evaluate and observe agents in production.
Build, deploy and present a production-ready enterprise AI agent platform.
The engineering builds you'll complete, pulled directly from the program's hands-on curriculum.
Design, build, deploy and present a production-ready enterprise AI agent platform featuring reasoning, memory, RAG, MCP-based tool integration, multi-agent orchestration, security, observability and AgentOps.
The program works across leading agent frameworks and infrastructure rather than locking you into one stack.
ChatGPT · Copilot · Claude · Gemini · Azure AI Foundry · Amazon Bedrock
OpenAI Agents SDK · AutoGen · LangChain · LangGraph · CrewAI · LlamaIndex
MCP · RAG · Function calling · Vector DBs · Embeddings · Memory · Knowledge Graphs
n8n · Power Automate · Zapier · Make · REST APIs · Enterprise Connectors
GitHub · Docker · Azure AI Foundry · OpenTelemetry · AgentOps · Evaluation & Monitoring tools
A preview of the modules inside the program. See the full breakdown in the detailed curriculum.
Agentic AI, enterprise architectures and design principles.
Goals, personas, reasoning, planning and task decomposition.
System prompts, structured outputs and context management.
Session, long-term, semantic memory; embeddings, vector databases and enterprise knowledge.
Model Context Protocol, APIs, enterprise connectors and function calling.
OpenAI Agents SDK, LangChain, LangGraph, CrewAI, AutoGen and LlamaIndex.
Agent collaboration, orchestration; event-driven workflows and business process automation.
Authentication, authorization, prompt injection defense, secrets management, human approval and Responsible AI.
Accuracy, reasoning quality, latency, hallucination detection; logging, tracing and monitoring.
Versioning, CI/CD, deployment, rollback, lifecycle management; scale AI agents in production.
Understand how AI agents work, tool calling, memory, orchestration, autonomy levels, and high-value business opportunities.
Architect multi-agent systems, error recovery, deterministic guardrails, human-in-the-loop triggers, and enterprise integration.
Customized capability journey for engineering and innovation teams scaling secure agentic automation across enterprise systems.
Every module, topic, activity and practical application, laid out in the detailed curriculum.