Who this course is for
For developers and engineers who want to move beyond ready-made AI tools and build their own production-ready LLM-based products – with custom MCPs, RAG, and agents, plus testing agents, handling security, running FinOps, and setting up Observability.
Backend, Frontend, and Fullstack engineers, AI/ML engineers, and tech leads with at least 2 years of professional development experience.
- I took the [AI-Driven Development]: Level 1 course and I want to go further – not just use ready-made tools, but build my own.
- We're building our own AI product with blackjack and agents.
- I need to roll complex AI features (agents, RAG, custom MCPs) into an existing product.
- I want to move AI prototypes out of Jupyter notebooks into reliable production – with observability, token cost control, quality assurance, and security.
What you'll gain
- Multiagent systems: designing architectures with multiple agents, splitting roles, and sharing context between them.
- Orchestration: the supervisor pattern, ReAct, and Agentic flows – planning loops and autonomous routing between steps.
- Hybrid designs: choosing the right architecture for the job and moving from a product prototype to enterprise scale.
- Custom MCPs: your own MCP servers in Python or TypeScript to integrate LLMs with any internal infrastructure.
- RAG and CAG: building vector databases (Qdrant), embeddings, prompt caching, and context management.
- The 2026 stack: Python/TypeScript, LangChain, LangGraph, Qdrant, n8n, Langfuse, Temporal, OpenAI API.
- Evals: approaches to testing LLM pipelines, LLM-as-a-Judge, and synthetic datasets.
- Observability: agent tracing in Langfuse, monitoring model quality and behavior in production.
- FinOps: controlling API spend, setting limits, the economics of an AI feature, and alerting.
- Security: defending against prompt injections, data leaks, and setting up RBAC for agents in a corporate environment.
- Common mistakes and misconceptions: a breakdown of the typical pitfalls when taking LLM features to production – from "hallucinations can be fixed with a prompt" to underestimating cost and security risks.
Course goals
ReAct, the supervisor pattern, and complex hybrid designs for scaling AI solutions to enterprise level.
Cache prompts (CAG) and build RAG systems on local vector databases – so the model works reliably with internal knowledge.
Write your own MCPs in Python or TypeScript to connect LLMs to any internal infrastructure – local databases, scripts, and internal APIs.
From an n8n prototype to production code on LangGraph: memory, state, planning loops, and autonomous routing between steps.
Stand up local vector databases (Qdrant), prepare a document corpus, compute embeddings, and tune prompt caching for production workloads.
Evals, LLM-as-a-Judge, synthetic data – ship AI to production with quality guarantees, not just gut feel.
Agent tracing in Langfuse, monitoring quality in prod, controlling API spend, and modeling the economics of an AI feature.
Prompt injections, data leaks, RBAC, and agent security in your product – closing every key risk.
What's on the course
- 1AI solution architecture
Orchestration, ReAct, Agentic flows. Complex hybrid agent designs and multiagent architectures to move from a single product to enterprise scale.
- 2Context management (RAG and CAG)
Context is King: prompt caching and Cache-Augmented Generation. When to build a vector database (RAG), standing up a local Qdrant, preparing documents, and computing embeddings.
- 3Agents and context
Anatomy of an agent: planning, memory, and state. AI chains and low-code automation in n8n – from prototype to a working assistant for SMB tasks.
- 4Advanced MCP
Building your own MCP servers in Python and TypeScript. Integrations with local databases, scripts, and internal APIs. MCP + skills + CLI – extending the AI IDE for product-grade work.
- 5Testing and Observability
Evals and generation-quality metrics, synthetic datasets, LLM-as-a-Judge. Agent tracing in Langfuse and analysis of model behavior in production.
- 6FinOps and Security
The economics of AI applications: controlling API spend, setting limits, and modeling the cost of a feature. Defending against prompt injections and data leaks, plus RBAC and agent security in a corporate environment.
Course practice
The course is built around developing your own multiagent AI system – from an n8n prototype to a production solution on LangGraph, with Observability, Evals, and cost control.
Every student picks one of four course tracks based on their professional domain and carries it through every stage – from system prompts to an agent shipped to production.
You write your own MCP servers in Python or TypeScript, stand up a local vector database (Qdrant), prepare a document corpus, and compute embeddings. You learn to tell when CAG is enough and when you actually need full-blown RAG.
You prototype 2–3 agents in n8n as a linear workflow, then port the logic to code on LangGraph – memory, state, looping, and autonomous routing between agents. You assemble a multiagent scenario with the supervisor pattern.
You add Evals on synthetic data, wire up tracing in Langfuse, set up spend alerting, and ship basic protection against prompt injections. You take agents to production with metrics, observability, and security.
Course assignments
- 1Module 1. Your own MCP server
You write a custom MCP server in Python or TypeScript for an internal database or script and plug it into Cursor / Windsurf.
- 2Module 2. RAG on Qdrant
You stand up a local Qdrant, prepare a document corpus, compute embeddings, and assemble a basic RAG with context caching.
- 3Module 3. Agent prototype in n8n
You prototype 2–3 agents from your course project as a linear workflow in n8n – with RAG and MCP tools already plugged in.
- 4Module 4. Porting to LangGraph
You port the agents from n8n into LangGraph code – adding loops, memory, and autonomous routing between steps and agents.
- 5Module 5. Multiagent scenario
You implement a supervisor pattern or ReAct architecture for three agents in your course project – with split roles and shared context.
- 6Module 6. Evals and tracing
You generate a synthetic dataset, write LLM-as-a-Judge tests, and wire up Langfuse for tracing agent chains.
- 7Module 7. FinOps and Security audit
You set up cost-per-feature tracking, token limits, and budget alerts. You run a security audit of your agent – prompt injections, data leaks, RBAC – and close every issue you find.
Certificates
Your final certificate depends on how much practice you complete and on the defense of your course project:
- "Audited" – the participant followed the course, attended the sessions, and went through the materials without completing the practice.
- "Completed" – at least 70% of homework done and a basic working setup in place: your own MCP, RAG, and an agent prototype in n8n.
- "Completed with Distinction" – all homework done and the course project successfully defended: your own multiagent shipped to production with Observability, Evals, and cost control.
Prerequisites
Confident use of an AI IDE (Cursor / Windsurf), basic prompt engineering, and a clear sense of when to use code-focused models versus reasoning models. A seamless step up from the Level 1 course.
Python or TypeScript at a professional level – at least 2 years of commercial experience. Willingness to write code, not just configure ready-made tools.
Hands-on experience with REST APIs, Docker, and databases. Willingness to run Qdrant locally and work from the CLI.
Learning process
Format
Theory, coding, and homework at your own pace. 6–8 hours per week, with all materials and repositories yours to keep forever.
Two meetings per week: course project reviews (architecture, code, product decisions) and Q&A with AI engineers.
A detailed code review by mentors with production experience, plus a live Telegram chat with AI engineers.
The end-to-end course project is a multiagent system of three agents that you ship to production. You can pick one of four tracks based on your professional domain:
The track you choose is built up in layers across your homework:
Course program
Module 1. Advanced MCP
Build your own MCP servers to connect LLMs to any internal infrastructure.
- MCP architecture: how the Model Context Protocol works, the specification, and the principles for building servers.
- Development in Python / TypeScript: writing MCP servers from scratch, connecting to local databases and scripts.
- MCP + skills + CLI: extending the AI IDE with your own tools and using AI for product work.
- Practice: writing an MCP server for an internal database and plugging it into Cursor / Windsurf.
Module 2. Context management (RAG and CAG)
Learn to manage model context – from caching to full-blown RAG on a local vector database.
- Cache-Augmented Generation (CAG): Context is King, prompt caching, and when CAG is enough.
- When to bring in a vector database: criteria for choosing between CAG and RAG, typical scenarios, and pitfalls.
- Local vector databases: standing up Qdrant, core operations, and picking an embedding model.
- Document preparation: chunking, cleanup, and metadata – preparing a corpus for RAG.
- Practice: optimizing queries and caches, plus a basic RAG pipeline on Qdrant with a document corpus.
Module 3. Agents and context
Break down the anatomy of an agent and assemble a working assistant for prototypes and SMB tasks.
- Anatomy of an agent: planning loops, tool use, and core architectures.
- State management: passing data between agent runs, short-term and long-term memory.
- AI chains in n8n: low-code automation and embedding LLMs into business processes.
- Practice: building an analyst agent from scratch and assembling a request-handling pipeline in n8n.
Module 4. AI solution architecture
From a single product to enterprise scale – designing complex multiagent systems.
- Orchestration and ReAct: architectural patterns, Agentic flows, and the reasoning + acting loop.
- Multiagent architectures: how multiple agents interact, the supervisor pattern, and role separation.
- Hybrid designs: applying architectures in production and picking the right approach for the job.
- Practice: assembling LLM reasoning logic and a multiagent system of several AI agents.
Module 5. AI frameworks and tools
An overview of production frameworks – moving from prototypes to full-fledged development.
- LangChain and LangGraph: comparing the ecosystems and picking the right framework for the job.
- Supporting technologies: Temporal for background and long-running tasks, plus process orchestration.
- Porting prototypes: migrating from n8n into LangGraph code while keeping the logic intact.
- Practice: porting 2–3 agents from your course project from n8n into LangGraph.
Module 6. Testing and Observability
Learn to ship LLM pipelines to production with metrics and visibility.
- Approaches to Evals: testing LLM pipelines and the key metrics for generation quality.
- Synthetic data: generating datasets with AI and writing LLM-as-a-Judge tests.
- Monitoring and tracing: LLM tooling (Langfuse and others) and tracing agent chains.
- Logging: analyzing model behavior in production and catching regressions.
- Practice: writing LLM-as-a-Judge tests and wiring tracing into a RAG system.
Module 7. FinOps and Security
Run the numbers on AI applications and close the main risks in a corporate environment.
- API cost control: token usage metrics and the main sources of overspending.
- Feature economics: cost per request, payback, and spend forecasting.
- Managing limits: per-user budgets, rate limits, and protection against runaway costs.
- Defending AI applications: prompt injections, data leaks, and the main attack vectors.
- RBAC and data security: scoping agent permissions and protecting PII and other sensitive data when working with LLMs.
- Practice: setting up spend alerting, plus a security audit of an AI agent with all findings closed out.
Why this course?
1. Production-ready, not Jupyter notebooks
The course teaches you to ship LLM solutions to real production – with metrics, monitoring, cost control, and protection against attacks – not leave them stuck at the notebook prototype stage.
Why this matters: This is the main gap between an AI enthusiast and an AI engineer. After this course, you can take on AI features with confidence that they'll actually reach users.
2. A seamless continuation of L1
The course picks up where L1 left off: from using ready-made tools to building your own. No filler – straight to the advanced material.
Why this matters: L1 students don't lose momentum and step right into serious development. You get the full picture – from your first .cursorrules all the way to a multiagent system in production.
3. Multiagent end to end
The full cycle: designing roles, prototyping in n8n, porting to LangGraph, multiagent orchestration, and shipping to production.
Why this matters: Multiagent systems are the leading technical trend of 2026. You walk away with your own working multiagent system in your portfolio – not a "hello world," but a real-world solution.
4. The 2026 production stack
LangChain, LangGraph, Qdrant, Langfuse, n8n, Temporal – the real tools the industry uses to build AI solutions right now.
Why this matters: The course gives you the stack production teams actually use. Not training-wheel tools, but the industry kit – so you immediately fit in on any team working with LLMs.
5. Not just development, but the wrapper around it
Evals, Observability, FinOps, and Security – four blocks that most AI courses simply don't cover.
Why this matters: Most courses teach you "how to write an agent," but not "how to live with it in production." This one closes all four gaps – tests, monitoring, money, and security.
6. A course project tailored to your domain
Four tracks – AI Editorial, Smart Tech Support, a development assistant, or a Personal Knowledge Curator. You build the project that fits your work.
Why this matters: You're not writing abstract training code – you're solving a real scenario from your professional domain. The finished project can go straight to your team or into your actual workflow.
Course instructors
Product Manager & AI Creator
- Senior Product Manager at an international B2B IT company
- Head of QA
- Building his own AI agents and MCP servers
- Speaker and mentor at AI workshops and hackathons
Technical Product Manager with 16+ years in IT. Started as a QA engineer, led a QA department for 10 years, then moved into product management. For the past 3 years he has been actively adopting AI into development processes, building his own products and agent systems. Author of a Telegram channel on AI tools.
Experience:Solution Architect & Founder of Hard&Soft Skills
- Solution Architect at international IT companies
- Instructor on the Tech Lead and Solution Architect courses at Hard&Soft Skills
- Consulting on AI solution architecture and multiagent systems
- Speaker at industry conferences on architecture and AI
Solution Architect and co-founder of Hard&Soft Skills with many years of experience in IT. Instructor on the Tech Lead and Solution Architect courses, expert in enterprise systems architecture and rolling AI into production at large IT companies.
Experience:Course price
- Full access to all 7 course modules
- Homework review
- Access to materials and recordings
- Course chat
- Electronic course completion certificate in EN and RU
- Full access to all 7 course modules
- Homework review
- Access to materials and recordings
- Course chat
- Electronic course completion certificate in EN and RU
- + 2 hours of personal consultations with the course instructor
