Still calling a single LLM call an “agent”? Real agentic systems plan, use tools, stay inside guardrails, and don't quietly blow through your API budget. This is the course that gets you there.
Learn agent frameworks, MCP, guardrails, and cost/latency optimization the way production AI teams actually deploy them — ending in a capstone agent you can show, not just a certificate.
By the time you finish this course, you will have built a multi-agent system with tool use and guardrails, optimized it for cost and latency, and deployed it as a monitored service — the same rigor production AI teams apply before an agent touches a real user. This course is how you get there in 8 weeks, building directly on Prompt Engineering Professional rather than re-teaching prompting and RAG.
Syllabus
Phase 1: Agent Frameworks & Tool Use (Weeks 1–4)
Modules
From Prompts to Agents & Tool Use
Intro to MCP (Model Context Protocol)
Agent Frameworks (LangGraph & CrewAI) & Memory
Multi-Agent Patterns
Guardrails & Applied Lab
Phase 2: Cost, Latency & Production Deployment (Weeks 5–8)
Modules
Cost & Latency Optimization
LLM Gateways & Routing
Observability & Evaluation for Agents
Capstone Project
Full session-by-session breakdown (all 24 sessions, 2-hour format) is in the downloadable curriculum PDF linked from the hero.
Outcome
By the end of this course, you will be able to:
- Design agents using the ReAct pattern and modern agent frameworks (LangGraph, CrewAI)
- Connect agents to tools and external systems, including MCP servers
- Apply multi-agent patterns: manager/worker, critic/verifier, parallel-plus-judge
- Build guardrails that scope tool permissions and prevent unsafe actions
- Optimize for cost and latency: caching, model routing, prompt compression
- Evaluate agents on task success rate and tool-call accuracy, not just final output
- Deploy an agent as a monitored production service
- Deliver a capstone project: a working, guarded, deployed agentic system
- Career roles you'll be ready for: AI Agent Engineer, LLM Ops Engineer, GenAI Platform Engineer
Tools
Python
LangGraph
CrewAI
MCP
OpenAI
Vector Databases
Docker
Git & GitHub
Who Should Enrol
Already through Prompt Engineering Professional?
Phase 1 skips straight past prompting and RAG basics into agent frameworks. No repeated fundamentals.
GenAI developer who's only built single-call LLM features?
Every concept is taught with a working lab — you'll leave with a real multi-agent system, not a single prompt-and-response loop.
Backend or platform engineer curious about agentic AI?
Phase 1 moves fast through agent fundamentals so you can focus on cost, latency, and deployment — the parts that map to your existing skills.
Considering the full Prompt Engineering & LLM Ops track?
This is Course 2 of 2. Complete Prompt Engineering Professional first if you haven't — this course builds directly on it.
Market Growth
AI Industry Growth
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Mid-Level AI Salaries
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Senior AI Compensation
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AI Job Openings by 2026
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FAQs
Prompt engineers and GenAI developers who want to build and operate multi-agent systems in production, not just single-prompt applications.
It’s recommended. This course assumes comfort with prompting, evaluation, and RAG fundamentals, and builds directly on top of that.
Model Context Protocol standardizes how agents connect to tools and data sources. It’s one of the most significant additions to the AI engineering stack in the last two years, and this course teaches it hands-on.
A capstone project: a deployed, guarded multi-agent system with cost/latency optimization and monitoring, presented as a live demo.
AI & ML Pathway teaches classical ML. Prompt Engineering Professional teaches prompting, evaluation, and RAG. LLM Ops & Agentic Systems teaches you to turn those into multi-agent systems that run safely and affordably in production.