Building Agentic AI From Workflows to Production
Building Agentic AI From Workflows to Production by Sinan Ozdemir is an advanced hands-on AI engineering course focused on building, evaluating, and deploying production-ready AI agents and multi-agent systems using modern frameworks like LangGraph, RAG, MCP, Docker, and tool-calling LLMs.
This program focuses on one major transformation:
👉 moving from simple AI prototypes to scalable, enterprise-grade agentic systems
Instead of stopping at prompts and basic chatbot workflows, this course teaches how to architect autonomous AI systems capable of reasoning, planning, retrieval, tool usage, multimodal understanding, memory, and production deployment.
What’s Included on Building Agentic AI
Inside this training, you get access to:
- 4+ hours of video instruction
- Production-ready AI workflow training
- LangGraph and RAG systems
- Multi-agent architectures
- AI evaluation frameworks
- Voice and multimodal agent systems
- Long-term memory implementation
- Enterprise deployment strategies
What You’ll Learn
Production-Ready AI Workflows
Learn how to:
- Build AI workflows using LangGraph
- Implement Retrieval-Augmented Generation (RAG)
- Create text-to-SQL systems
- Use vector databases and embeddings effectively
- Evaluate retrieval and generation quality
You’ll also benchmark multiple LLMs across:
- Accuracy
- Latency
- Cost
- Success rate
From Workflows to Autonomous Agents
Transform static workflows into autonomous ReAct agents capable of:
- Tool calling
- Evidence retrieval
- SQL execution
- Dynamic reasoning loops
You’ll Learn
- Tool-calling LLM systems
- Agent evaluation metrics
- Cost-per-task optimization
- Token efficiency analysis
- Rubric-based LLM grading
Multi-Agent Systems in Practice
Master advanced multi-agent architectures including:
- Sequential agents
- Aggregator systems
- Routing architectures
- Supervisor agents
- Any-to-any communication systems
Real Project Included
Build a complete AI SDR system using:
- MCP
- Docker
- CRM automation
- Email outreach workflows
- Research agents
Multimodal & Coding Agents
Move beyond text-based AI systems and build:
- Coding agents using raw Python execution
- Visual AI systems using Moondream
- Computer-use agents
- Voice AI pipelines with real-time STT/TTS
Technologies Covered
- Groq Whisper
- Twilio
- Llama-4-Scout
- Moondream
Future Directions in Agentic AI
Learn enterprise-scale AI implementation topics such as:
Long-Term Memory Systems
- Persistent memory architectures
- Self-improving agents
- Extended Mind-inspired systems
AI Guardrails & Security
- Constitutional AI constraints
- Input/output validation systems
- MCP security risks
- Tool poisoning prevention
- Command injection mitigation
Building Agentic AI From Workflows to Production Course Curriculum
Lesson 1 – Production-Ready AI Workflows
- LangGraph workflows
- RAG implementation
- SQL generation systems
- Vector search and embeddings
- Retrieval evaluation
Lesson 2 – From Workflows to Agents
- ReAct agents
- Tool-calling LLMs
- Agent reasoning loops
- Explicit and implicit evaluation metrics
Lesson 3 – Agentic Systems in Practice
- Multi-agent design patterns
- AI SDR architecture
- Docker + MCP workflows
- Prompt engineering optimization
Lesson 4 – Multimodal, Reasoning & Coding Agents
- Coding agents
- Visual understanding systems
- Voice agents
- Computer-use AI systems
Lesson 5 – Future Directions in Agentic AI
- Long-term memory
- Guardrails and constitutional AI
- Enterprise deployment frameworks
- Agent scalability systems
Why Choose this course
Most AI courses focus only on prompts.
This one focuses on:
✅ Production deployment
✅ Agent evaluation
✅ Multi-agent architectures
✅ Enterprise AI systems
✅ Security and scalability
Key advantages:
- Real engineering implementation
- Modern AI stack coverage
- Strong evaluation frameworks
- Enterprise deployment focus
- Hands-on practical systems
Key Benefits
- Build production-grade AI agents
- Create scalable multi-agent systems
- Learn advanced RAG workflows
- Implement multimodal AI systems
- Understand AI evaluation and benchmarking
- Deploy enterprise-ready agentic systems
Who This Course Is For
- AI engineers
- Machine learning engineers
- Developers and software architects
- Technical product managers
- Data scientists building AI systems
About the Creator
Sinan Ozdemir is known for teaching practical machine learning, AI engineering, and production AI deployment systems with a focus on scalable real-world applications.
Final Thoughts
Building Agentic AI From Workflows to Production by Sinan Ozdemir is one of the most practical and technically complete AI engineering programs for developers who want to move beyond prototypes and build real-world agentic systems.
If you want to master AI workflows, multi-agent architectures, autonomous systems, multimodal agents, and enterprise AI deployment, this course provides a highly implementation-focused roadmap.
Get Building Agentic AI From Workflows to Production by Sinan Ozdemir.
