Highlights
- Explore Python Fundamentals
- Build an Agent Core
- Work with LLMs
- Create Conversational AI & Multimodal Apps
- Build MCP Servers
- Learn RAG Fundamentals
- Design Multi-Agent Systems
- Implement Agent Memory & Workflows
- Work with Structured Facts
- Develop Adaptive Retrieval
- Handle Production Concerns
- Use LangChain with Python
- Build a Capstone Project
Course Details
Module 1 — Python Fundamentals - Optional module.
- Work fluently with Python data structures, modules/packages, CLI args, and logging.
- Model domain objects with dataclasses and define contracts with Protocols.
- Write async code: asyncio tasks, queues, timeouts, and cancellation.
- Build and test a basic HTTP API with FastAPI and httpx.
Module 2 — Agent Core
- Understand the message format that drives an agent (system, user, assistant, tool messages).
- Build a tool-calling loop: schema in, action out, result back, repeat.
- Implement a tool registry with schema validation, routing, and error handling.
- Add safety rails (allowlists, rate limits, redaction) and an evaluation harness with golden tests.
Module 3 — Working with the LLM
- Call the LLM chat-completion API directly — message roles, parameters, streaming.
- Build a CLI chat loop with conversation history and a FastAPI streaming endpoint with SSE.
- Implement session storage: in-memory first, then file-based.
- Apply prompting patterns that hold up in production (structured outputs, grounding).
Module 4 — Conversational AI + Multimodal
- Build a conversational AI application that integrates the agent core, LLM calls, and prompt engineering from earlier modules.
- Apply prompt engineering: system prompts, few-shot examples, structured outputs, and grounding.
- Work with multimodal inputs and outputs: vision/image analysis, speech-to-text, text-to-speech.
- Add guardrails: model selection, token budgeting, content filters, and confidence thresholds.
Module 5 — MCP Servers
- Understand MCP concepts: tool discovery, schemas, calling conventions.
- Build a minimal MCP server in Python, then add practical tools.
- Implement real-world tools: data lookups, queries, sensor reads.
- Add per-tool auth scopes and structured logging for observability.
Module 6 — RAG Fundamentals
- Design chunking strategies (size, overlap, structure-aware splits) for long documents.
- Produce embeddings, store them in a vector index, and query effectively.
- Apply retrieval strategies: hybrid search, metadata filters, reranking, and grounded answers with citations.
- Run basic RAG evaluation: recall/precision on retrieval and adversarial queries.
Module 7 — Multi-Agent Systems
- Decide when multi-agent designs are worth the latency and operational complexity.
- Model roles (router, researcher, coder, critic) and coordination patterns (supervisor, swarm, debate).
- Share context and tools safely across agents and apply conflict resolution / consensus strategies.
Module 8 — Agent Memory + Workflows
- Implement short-term memory (conversation/session) separate from long-term profile or notes.
- Apply summarisation to fit context limits; model decay and explicit "do not remember" controls.
- Compare workflow patterns: ReAct, plan-and-execute, and tool routing for multi-step tasks.
Module 9 — Structured Facts
- Use structured outputs (Pydantic models, JSON Schema) to get reliable, typed data from an LLM.
- Build a fact extraction pipeline that decomposes text into individual claims with provenance.
- Construct a knowledge graph from extracted entities and relationships.
- Implement grounded QA that answers questions from the graph and cites source documents.
Module 10 — Adaptive Retrieval
- Build a retrieval router that selects the right source (vector store, knowledge graph, keyword search) based on query type.
- Implement query decomposition — breaking complex questions into focused sub-queries.
- Add a self-critique loop (corrective RAG) that evaluates retrieved documents and re-retrieves when quality is low.
- Orchestrate multi-source retrieval — merging results from different backends with relevance scoring.
Module 11 — Production Concerns
- Add tracing and structured logging so every tool call is attributable and debuggable.
- Define metrics and timelines that surface latency, errors, and dependency health.
- Implement reliability (retries, timeouts, circuit breakers, fallbacks) and cost controls (caching, batching, token budgets).
- Outline deployment: containers, running MCP alongside HTTP APIs, and config per environment.
Module 12 — LangChain with Python
- Understand what LangChain provides vs building from scratch: chains, agents, tools, memory, and output parsers.
- Rewrite a hand-rolled agent loop using LangChain components and compare the trade-offs.
- Connect LangChain to an MCP server and RAG pipeline built in earlier modules.
Module 13 — Capstone Project
- Build a full agentic application: chat UI or CLI, RAG, MCP tools, and a coordinated multi-agent path for complex questions.
- Write demo scenarios and integration tests that guard against regressions.
- Document extension points for adding tools, data sources, and policies.
Who should attend
- Developers who want to build AI-powered or agent-based applications
- Python programmers looking to level up with LLMs and modern AI tooling
- Backend or full-stack engineers interested in integrating AI into products
- Data engineers and ML practitioners exploring practical LLM systems
- Technical founders or product builders creating AI-driven solutions
Feedback
4.8 out of 5 average
⭐⭐⭐⭐⭐ 5.0/5
"The course moved far beyond basic AI usage and demonstrated advanced techniques that can genuinely transform productivity. We learned how to structure complex prompts, automate documentation tasks, analyse information more effectively, and integrate AI into existing workflows. The practical nature of the course ensured that everyone could immediately apply what they learned."
David Wilson - Lead Systems Engineer - Engineering Consultancy
"The AI Agents course provided an outstanding introduction to automation using modern AI technologies. The trainer explained complex concepts clearly and demonstrated practical use cases that could be implemented within our organisation. The hands-on exercises helped us understand how AI agents can support business processes and improve operational efficiency. We left with a clear roadmap for future automation initiatives." Daniel Foster - Solutions Architect -Technology Services Provider
