Agentic AI Course
A job-ready agentic AI curriculum built on software engineering, LLMs, AI agents, cloud and production skills. Live cohort classes plus 1:1 mentorship.
To become a job-ready Agentic AI Engineer you need a combination of software engineering, LLMs, AI agents, cloud, and production skills. This program takes you through all of them — with agentic systems, RAG, and MCP at the core.
- 18 modules
- Agentic, RAG & MCP at the core
- Evaluation & security built in
- FastAPI, LangGraph, Docker, cloud
Why this program
What makes this different from the usual online courses and bootcamps.
One professional, all the way through
The entire program is delivered by a single practitioner — not split across different instructors. Every student learns at the same pace, hears the same voice, and is held to the same standard. No one falls through the cracks.
Agentic systems, not surface-level AI
Most courses stop at prompting and RAG. This program goes all the way — agent architectures, MCP, evaluation pipelines, security, and production observability.
Build, don't just watch
Every module maps to a hands-on project or practical exercise. You leave with real agents, real deployments, and real code — not just notes.
Job-ready, not job-hopeful
12 months of structured learning that maps directly to Agentic AI Engineer roles — not a certificate that sits on your LinkedIn collecting dust.
Curriculum
18 modules. Highlights marked with a star are the program's priority areas.
Programming Fundamentals
The language and tooling baseline you will use every day as an AI engineer.
- Python
- JavaScript / TypeScript
- Object-Oriented Programming
- Data Structures & Algorithms
- Git & GitHub
- Linux / Command Line
- REST APIs
- JSON
- Async programming
- Error handling & debugging
- Testing
AI & Machine Learning Fundamentals
The ML, deep learning, and NLP grounding you need before working with LLMs.
- Machine Learning basics
- Deep Learning basics
- Neural Networks
- NLP fundamentals
- Transformers
- Embeddings
- Model inference
- Fine-tuning fundamentals
- PyTorch
- Hugging Face
LLM Engineering
Using large language models as dependable building blocks in real systems.
- LLM architecture
- OpenAI / Anthropic / Gemini APIs
- Prompt engineering
- Context engineering
- Structured outputs
- Function / tool calling
- Streaming
- Token management
- Model selection
- LLM cost optimization
- LLM latency optimization
RAG & Knowledge Systems
Grounding models in real, up-to-date knowledge so answers come from your data.
- RAG (Retrieval-Augmented Generation)
- Advanced RAG
- CAG (Context-Augmented Generation)
- Embeddings
- Vector databases
- Semantic search
- Hybrid search
- Reranking
- Chunking strategies
- Metadata filtering
- Knowledge graphs
- Document processing
- Multimodal RAG
Tools
- FAISS
- Qdrant
- Pinecone
- Weaviate
- Chroma
- Elasticsearch / OpenSearch
Agentic AI
The core skill area of this program — designing systems where models plan, act, and learn from outcomes.
- AI agent architecture
- Agent loops
- Planning
- Reasoning
- Tool use
- Function calling
- Tool selection
- Agent memory
- Short-term & long-term memory
- Reflection
- Self-correction
- Task decomposition
- Goal-oriented agents
- Autonomous workflows
- Human-in-the-loop systems
- Multi-agent systems
- Agent orchestration
- Agent handoffs
- Agent routing
- State management
Agent Frameworks
Know one or two frameworks deeply rather than knowing everything superficially.
- LangGraph
- LangChain
- CrewAI
- AutoGen
- OpenAI Agents SDK
- Google ADK
- LlamaIndex
MCP — Model Context Protocol
A very useful modern skill — connecting agents to external applications and data.
- MCP fundamentals
- MCP servers
- MCP clients
- MCP tools
- MCP resources
- MCP prompts
- Authentication
- Building custom MCP servers
- Connecting agents to external applications
- MCP security
AI Workflow Automation
Automating real business processes end-to-end with agents and APIs.
- n8n
- Make
- Zapier
- API automation
- Webhooks
- Event-driven workflows
- Browser automation
- Database automation
- Email automation
- CRM automation
- Document automation
- Business-process automation
Databases
SQL, NoSQL, and vector stores — and knowing when to reach for each.
- SQL
- PostgreSQL
- MySQL
- Query optimization
- NoSQL
- MongoDB
- Redis
- Vector
- Qdrant
- Pinecone
- Weaviate
- FAISS
Backend Engineering
Shipping reliable APIs and services that power your agents in production.
- FastAPI
- Flask
- Node.js
- REST APIs
- GraphQL basics
- WebSockets
- Authentication
- OAuth
- JWT
- API gateways
- Microservices
- Background jobs
- Queues
Cloud & Deployment
At least one major cloud platform, plus containers and deployment automation.
- AWS — EC2, S3, Lambda, ECS/EKS, Bedrock
- Azure — AI, OpenAI, Functions, AKS
- Google Cloud — Vertex AI, Cloud Run, GKE
- Docker
- Kubernetes
- CI/CD
- GitHub Actions
- Terraform basics
- Serverless architecture
MLOps / LLMOps
Getting models and prompts into production and keeping them healthy once there.
- Model deployment
- Model versioning
- Experiment tracking
- Prompt versioning
- Dataset management
- Model monitoring
- LLM monitoring
- Cost monitoring
- Latency monitoring
- Logging
- Tracing
- CI/CD for AI
- Evaluation pipelines
Tools
- MLflow
- LangSmith
- Weights & Biases
- Arize Phoenix
- OpenTelemetry
AI Evaluation
Becoming extremely important for production agents — measuring quality before users do.
- LLM evaluation
- Agent evaluation
- RAG evaluation
- Hallucination detection
- Groundedness
- Faithfulness
- Relevance
- Accuracy
- Tool-use evaluation
- Regression testing
- Benchmarking
- Human evaluation
- Automated evaluation
- Red teaming
Metrics
- Precision
- Recall
- Faithfulness
- Context relevance
- Answer relevance
- Task success rate
- Tool-call accuracy
AI Security
Understanding how agents get attacked — and how to harden them.
- Prompt injection
- Jailbreaks
- Data leakage
- Agent hijacking
- Tool abuse
- Excessive permissions
- Authentication
- Authorization
- Secrets management
- PII protection
- Secure API design
- Sandboxing
- Guardrails
- AI safety
Observability
Answering the questions that keep agents safe: what did it do, why, how much it cost, and where it failed.
- Logging
- Tracing
- Metrics
- Agent trajectories
- Token monitoring
- Latency monitoring
- Error tracking
- Cost tracking
- Distributed tracing
Multimodal AI
Working with images, speech, and documents — not just text.
- Vision models
- Speech-to-text
- Text-to-speech
- Image understanding
- Document understanding
- OCR
- Video understanding
- Multimodal agents
Advanced Agent Architecture
The patterns senior engineers reach for when one agent is not enough.
- Multi-agent architectures
- Hierarchical agents
- Supervisor agents
- Planner-executor architectures
- ReAct
- Reflection agents
- Tool-using agents
- Long-running agents
- Stateful agents
- Event-driven agents
- Agent swarms
- Human-in-the-loop
- Agent-to-agent communication
- Memory architectures
Business & Product Skills
An excellent Agentic AI Engineer is more than a coder — you need to frame problems before solving them.
- Requirement gathering
- Process analysis
- Identifying automation opportunities
- AI ROI calculation
- System design
- API integration
- Product thinking
- User experience
- Technical documentation
- Client communication
Frequently asked questions
The questions engineering students ask most before enrolling.
Engineering: Agentic AI Engineer
This program is live on the platforms page — enroll to track your progress module by module.
Go to this program