Live Online Training
Agentic AI & Multi Agent Autonomous Systems Course
The most advanced specialization at Mindvex. Twelve weeks on building autonomous LLM agents and multi-agent architectures. LangGraph, CrewAI, AutoGen, plus framework-free construction. Cohort capped at 15 — the smallest in the catalog because the material requires direct instructor engagement with every learner’s work.
Live Online Training
Cohort Cap: 15 Students
LangGraph + CrewAI + AutoGen
First-Mover Specialization
Trusted by Learners & Professionals from Top Companies
WHY LEARN AGENTIC AI TODAY?
The Most Consequential Shift in Applied AI Right Now
- Agentic AI is the fastest-adopted architecture in the US AI market
- No major US competitor offers an equivalent live cohort-based course at this depth
- Master LangGraph, CrewAI, AutoGen, and framework-free agent construction
- Small cohort of 15 for direct instructor engagement with every learner's work
- Early practitioners in an emerging specialty — first-mover positioning for the market
WHAT YOU'LL LEARN
Agent Architectures
Tool Use & Tool Design
Multi-Agent Orchestration
Memory Systems for Agents
LangGraph State Machines
Agent Observability
Safety & Guardrails
Production Agent Engineering
FRAMEWORKS & TOOLS YOU'LL MASTER
LangGraph
Headline framework for stateful agent orchestration
LangChain
Widely adopted LLM application framework
CrewAI
Role-based multi-agent framework
AutoGen
Conversation-driven multi-agent systems
OpenAI API
Direct agent construction on GPT models
Anthropic API
Claude for reasoning-heavy agents
LangSmith
LLM observability and tracing
Langfuse
Open-source LLM observability
Pinecone
Vector memory at scale
Weaviate
Alternative vector database
FastAPI
Deploying agent systems as services
Docker
Containerizing agent applications
COURSE CURRICULUM
01
02
03
04
05
Week 1
Agent Foundations
- The agent paradigm
- ReAct reasoning-action loops
- Tool use design
- Memory architectures
- Planning and decomposition
Week 4
Multi-Agent Architectures
- Supervisor-worker patterns
- Peer-to-peer coordination
- Specialized agent roles
- Inter-agent communication
- Debate and verification
Week 6
LangGraph & Frameworks
- LangGraph state machines
- CrewAI role-based agents
- AutoGen conversation flows
- Framework-free construction
- Framework selection judgment
Week 8
Production Engineering
- Agent observability
- Debugging non-deterministic systems
- LLM-as-judge evaluation
- Cost engineering for agents
- Reliability patterns
Week 11
Advanced Agent Topics
- Long-horizon autonomous behavior
- Memory at scale
- Multi-modal agents
- Code execution agents
- Browser and computer-using agents
What You Will Learn
The Twelve-Week Curriculum
The curriculum is organized into four arcs across twelve weeks. The course is heavily project-based — every arc produces working agent systems, and the course culminates in a substantial end-to-end multi-agent capstone.
The first arc establishes the conceptual and architectural foundation for agentic AI. Many engineers approach agents as a thin layer on LLM API calls and find that working agent systems require fundamentally different design discipline.
Topics include the agent paradigm; ReAct and reasoning-action loops; tool use design (schemas, error handling, common failure modes); function calling and structured outputs in agent contexts; memory architectures (short-term, episodic, semantic); planning and decomposition; and reflection and self-correction patterns.
The arc ends with the first project: a working single-agent system that uses multiple tools, maintains memory, and handles errors gracefully.
You will Explore
ReAct Loops
Tool Use Design
Memory Architectures
Planning Patterns
The second arc moves from single agents to multi-agent systems — where architectural decisions become most consequential and where the gap between working systems and impressive demos becomes most visible.
Topics include multi-agent design patterns (supervisor-worker, peer-to-peer, hierarchical); specialized agent roles (researcher, planner, executor, critic); inter-agent communication; debate and verification patterns; and agent orchestration frameworks in depth (LangGraph, LangChain agents, CrewAI, AutoGen, framework-free construction). State management at scale rounds out the arc.
You will Explore
Supervisor-Worker Patterns
LangGraph Deep Dive
CrewAI & AutoGen
Framework-Free Construction
The third arc covers the engineering practices that distinguish production agent systems from prototypes. This material is where most agent demos fail in real deployment.
Topics include observability for agent systems (LangSmith, Langfuse); systematic debugging patterns; evaluation methodology for agents (trajectory-level, LLM-as-judge, golden datasets, human evaluation); cost engineering; latency optimization; reliability engineering; safety and guardrails for autonomous systems; and human-in-the-loop patterns.
You will Explore
Agent Observability
Trajectory Evaluation
Cost & Latency Engineering
Safety & Guardrails
The final arc covers advanced topics and dedicates time to the substantial multi-agent capstone project.
Topics include long-horizon autonomous behavior; memory at scale (vector memory, graph memory); multi-modal agents; code execution agents; browser-using and computer-using agents; and specialized application domains (customer support, research workflows, sales operations, software development).
Long-Horizon Autonomy
Multi-Modal Agents
Code-Executing Agents
Browser-Using Agents
Each learner builds an end-to-end autonomous multi-agent system, scoped to a real problem of substantive complexity.
Multi-Agent Capstone
Architect • Build • Ship
A substantial multi-agent system addressing a real problem. Recent capstones: autonomous research assistants, multi-agent customer support, agent-based code review systems, domain-specialized analytical agents.
By the end of twelve weeks, you'll have shipped a real autonomous multi-agent system — the kind of portfolio piece the most competitive US AI engineering roles specifically look for.
REAL-WORLD PROJECTS
Single-Agent with Tools
ReAct loop, multi-tool, error handling, memory.
Multi-Agent Research System
Coordinated specialized agents on a research task.
LangGraph State Machine
Production-grade stateful agent workflow.
Agent Observability Setup
Full tracing, monitoring, and evaluation for a live agent.
Multi-Agent Capstone
End-to-end autonomous system for a real problem.
Course Format and Delivery
The course is delivered fully online through live, instructor-led sessions taught by a working agent systems practitioner from the US AI engineering market.
01
Schedule
Four live sessions per week over twelve weeks, in evening and weekend slots that accommodate working professionals across US time zones.
- Each session runs approximately 90 minutes.
02
Cohort Cap: 15 Students
The smallest cohort cap in the Mindvex catalog. The cap exists because the material requires direct instructor engagement with every learner’s work.
- Smallest cohort. Deepest engagement.
03
Live System Building
Sessions are heavily applied. Learners build agent systems during sessions, not just watch demonstrations.
- Build. Test. Deploy.
04
Code Reviews
Major projects and the capstone receive detailed code review with written feedback. Code review is one of the most valuable parts of the course given the architectural complexity of agent systems.
- Architecture-level feedback.
05
First-Mover Positioning
The specialty is new enough that credentials are being established by early practitioners themselves. Graduates are among the early practitioners in the field.
- Enter an emerging market early.
Prerequisites And Technical Requirements
This is an advanced course with real prerequisites. Learners who do not meet them tend not to finish.
Strong LLM application experience — API integration, structured outputs, RAG
Production Python — async, type hints, testing, engineering discipline
ML foundation helpful (ML Bootcamp) but not strictly required
Plan for 12–16 hours per week (4 live sessions + independent practice)
Why Agentic AI Skills Matter In The Us Job Market
The Most Undersupplied AI Specialty.
Agent engineering is currently one of the most strongly differentiated specialties in the broader AI engineering market. Demand from AI-focused companies significantly outpaces the supply of practitioners with genuine agent engineering capability.
Compensation Among the Strongest in Software.
Total compensation at well-funded AI companies for senior agent engineering roles regularly reaches well into seven figures, with the highest end of the market commanding exceptional packages. Demonstrable capability — portfolio work, technical depth in interviews — is what distinguishes candidates.
First-Mover Advantage.
The specialty is new enough that credentials are being established by early practitioners themselves. Graduates completing this course in 2026 enter a market where demand is strong, supply is limited, and demonstrable capability is what unlocks the strongest positions.
Learn From Industry Leaders
Michael Anderson
Lead Instructor — AI & Cloud
- AWS Certified
- 10+ Years Industry Experience
- Published AI Researcher
Senior AI engineer with extensive experience designing machine learning platforms and deploying enterprise AI solutions.
David Miller
Cloud & MLOps Instructor
- Cloud Architect
- Google Cloud Certified
- Enterprise MLOps Specialist
Cloud Architect specializing in scalable AI infrastructure, MLOps automation, and multi-cloud deployments for enterprise.
INDUSTRY RECOGNIZED CERTIFICATE
Receive an industry-recognized certificate after successful course completion.
- Industry Recognized
- LinkedIn Shareable
- Verifiable Certificate
- Career Boost
Career Impact
What Our Learners Say
The AI for Data Analytics course gave me the exact tools needed to interpret complex data streams quickly. The practical framework for integrating modern AI directly into existing analytical workflows was immediately applicable.
The multi-agent simulations in the Advanced Python for LLM & Multi Agent Systems course completely transformed our engineering team's approach to complex system design. Exceptional depth and real-world utility.
Enrolling our product team in Agentic AI & Multi Agent Autonomous Systems provided the strategic edge we were missing. The hands-on project structures made deploying autonomous agents far more straightforward.
After taking NLP with LLMs, I was able to fine-tune and deploy a custom retrieval-augmented model in weeks. The instructors cover production-grade deployment strategies that you rarely find in standard online courses.
The Executive Diploma in Deep AI & Cloud Intelligence offered a clear, high-level strategic overview without glossing over technical rigor. It helped our leadership team align our enterprise cloud architecture with modern AI solutions.
Bridging the gap between prototype models and production infrastructure can be tough, but MLOps & AI in Production laid out the best practices clearly. Highly recommended for any engineering group scaling AI services.
Ready To Build At The Frontier?
Join one of the smallest, most advanced cohorts in the Mindvex catalog.
Frequently Asked Questions
General LLM courses cover single-LLM applications — prompts, RAG, structured outputs, fine-tuning. This course assumes that foundation and focuses on multi-step autonomous systems and multi-agent architectures. Qualitatively different material.
LangGraph is the headline because it has emerged as the most production-ready option for stateful agent orchestration. CrewAI and AutoGen are covered for cases where they are preferable. Framework-free construction using underlying APIs is also covered, because that is what production teams often build when frameworks become limiting.
Only with equivalent LLM application experience from work or self-study. The course assumes substantial LLM engineering foundation.
Yes. Computer-using and browser-using agents are covered in Arc 4, with architectural patterns, implementation, and safety patterns particularly important for agents interacting with computer interfaces.
Fundamental architectural patterns (agent loops, tool use, memory, planning, multi-agent coordination) are stable enough to teach reliably even as specific frameworks change. Specific frameworks and model capabilities updated each cohort.
The certificate is one signal. The more important signal is capstone and portfolio work. Hiring teams evaluate candidates on demonstrated technical work — code, project repositories, technical depth in interviews.
Gives a working foundation supporting either applied or research-adjacent path. Pure research careers may want to supplement with formal RL and autonomous-systems research literature.
The 4-sessions-per-week format with 12–16 hours independent practice is the most intensive commitment in the Mindvex catalog. Sustainable alongside full-time work for committed learners but requires deliberate scheduling.
- Cohort of 15
- LangGraph + CrewAI + AutoGen
- Multi-Agent Capstone
- First-Mover Positioning