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Building AI Agents and Chatbots training course

Name: Building AI Agents and Chatbots training course
Availability: InStock

A hands-on course that takes you from core Python and LLM basics to designing, building & deploying production-grade AI agents with tools, memory, retrieval (RAG), multimodal capabilities, & multi-agent systems.

⭐⭐⭐⭐⭐ 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

Public Courses

03/08/26 - 3 days

£2500 +VAT

Enrol

14/09/26 - 3 days

£2500 +VAT

Enrol

26/10/26 - 3 days

£2500 +VAT

Enrol

Customised Courses

* Train a team
* Tailor content
* Flex dates

From £1200 / day

Highlights Details Audience Feedback Prices/Dates Related Courses

Public Courses

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

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

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More about this course

This course teaches you how to design and build modern AI-powered applications using Python and large language models. You’ll learn how to create intelligent agents, integrate tools, and work with real-world data using techniques like RAG.

The curriculum covers everything from core fundamentals to advanced systems like multi-agent architectures and adaptive retrieval. You’ll also explore production-ready practices, including reliability, observability, and deployment strategies.

By the end, you’ll build a complete, agentic application that combines LLMs, APIs, and scalable workflows.

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