30-40 hour hands-on technical programme to build autonomous AI agents in production. LangChain, AutoGen, CrewAI, MCP servers and cost optimisation with DeepSeek and open-source models. Designed for tech leads, innovation CTOs and product managers.
If your technical team recognises any of these problems, this programme is designed to solve them with working code.
Framework fragmentation: LangChain, AutoGen, CrewAI, Semantic Kernel — a new framework every week and your team does not know which to adopt for production.
Evaluation gap: AI agents pass demos but fail in production due to a lack of standardised evaluation frameworks and reliability metrics.
Uncontrolled costs: LLM API calls in agent loops generate unpredictable bills without an optimisation strategy using open-source models and DeepSeek.
Deployment complexity: moving from a notebook of AI agents to an orchestrated multi-agent system with MCP servers and tool use requires architecture that tutorials do not cover.
Four AI agent architectures you will deploy to production during the programme, with working code and integrated evaluation.
Design and deploy systems where multiple AI agents collaborate with specialised roles: researcher, analyst, writer, validator. Delegation patterns, shared memory and conflict resolution between agents.
Build AI agents that use external tools (APIs, databases, code) and combine retrieval-augmented generation with step-by-step reasoning. ReAct patterns, function calling and tool chains.
Implement MCP (Model Context Protocol) servers to standardise communication between AI agents and corporate tools. Custom MCP server, integration with existing systems and context federation.
Implement evaluation frameworks to measure reliability, latency, cost per task and response quality of your AI agents. Production metrics, degradation alerts and observability dashboards.
Each module combines minimal theory with working code. The final deliverable is a deployed and evaluated multi-agent AI system.
The programme is designed for developers with Python experience and basic familiarity with LLM APIs (OpenAI, Anthropic). No prior experience with agent frameworks is required. The first modules cover fundamentals before moving to advanced architectures.
LangChain (agents, tools, chains), AutoGen (multi-agent orchestration), CrewAI (specialised agent pipelines) and MCP (Model Context Protocol). We also cover DeepSeek, Llama and Mistral for cost optimisation, and evaluation frameworks like RAGAS and DeepEval.
Yes. The programme includes real deployment: containerisation, secrets management, rate limiting, retry policies and monitoring. The final deliverable is a deployed, functional multi-agent system, not a notebook prototype.
A full module is dedicated to cost optimisation: intelligent model routing (GPT-4o for complex reasoning, DeepSeek/Llama for routine tasks), response caching, request batching and cost/quality analysis per task.
Yes. Before each programme we run a technical discovery session to tailor use cases, integrations and the final project to your company's existing stack and systems. We work with your repository and your APIs.
Strategic clarity on AI for C-Level: ROI, regulatory risk and investment decisions. Executive sessions 8-16h.
CV screening with ChatGPT, workplace climate analysis with AI and onboarding automation. 20-30 hours.
Complete guide to funding options for your technical AI agents training programme.
Digital training with personalised AI tutor, live classes, gamification and compliance built in.
Hands-on technical programme with LangChain, AutoGen, CrewAI and MCP. Response within 48 hours.
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