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Intermediate
AI Agentic Engineering Fundamentals
Learn to design and build AI-powered applications, agents, and multi-agent systems using Java. You'll implement RAG pipelines, function calling, agentic workflows, and production-ready patterns using Spring AI, LangChain4j, and Embabel.
Course Curriculum
- How large language models work
- Tokens, context windows & temperature
- Prompt engineering fundamentals
- Model capabilities & limitations
- Choosing the right model for the task
- Spring AI setup & model configuration
- ChatClient & prompt templates
- Streaming responses
- Structured output with bean mapping
- Multimodal inputs (image, audio)
- Advisors & conversation memory
- LangChain4j setup & chat models
- AI Services & declarative interfaces
- Structured prompts with @SystemMessage
- Response parsing & type-safe output
- Chat memory & message history
- Defining tools & @Tool annotation
- Tool execution & result injection
- Parallel & sequential tool calls
- Error handling in tool execution
- Security considerations for tool use
- Embeddings & semantic similarity
- Vector stores — PGVector, Chroma, Weaviate
- Document ingestion & chunking strategies
- Retrieval pipeline & similarity search
- Re-ranking retrieved results
- Evaluating RAG quality
- Conversation memory types — window, summary
- Persistent memory with databases
- Token budget management
- Context compression techniques
- Session isolation & multi-user memory
- The agent loop — observe, plan, act
- ReAct (Reasoning + Acting) pattern
- Multi-step task decomposition
- State management across steps
- Human-in-the-loop checkpoints
- Handling agent failures & retries
- Embabel architecture & goals
- Defining agents & their capabilities
- Supervisor & worker agent pattern
- Parallel agent execution
- Agent communication & result aggregation
- MCP protocol overview
- Building an MCP server in Java
- Exposing tools & resources via MCP
- Consuming MCP servers from agents
- MCP security & authentication
- Observability — tracing LLM calls
- Cost management & token budgets
- Latency optimisation & prompt caching
- Evaluating AI application quality
- Testing AI applications with deterministic assertions