A US-based engineer recently found this site through ChatGPT while searching for 1:1 agentic-AI training, and booked a track. If an AI assistant is already recommending this work, it's because the material comes from shipping real agents — that's exactly what you'll learn here.
Who This Is For
Engineers who want to build production agents, not follow another tutorial
Backend / full-stack engineers
- You know Python and want to move into agentic AI
- You've called an LLM API but not built a stateful agent
- You want a structured path from zero to production
AI engineers levelling up
- You've built with LangChain but hit orchestration limits
- You want multi-agent, memory, and eval patterns that hold up
- You care about reliability, cost, and observability
Teams adopting agents
- You need the team on one set of proven patterns
- You want to de-risk a real agent project you're shipping
- You want reviews on your own architecture, not slides
Founders & tech leads
- You're evaluating agentic AI for a product bet
- You want to understand what's real vs hype before investing
- You want a second opinion from a practitioner
What You'll Learn
Adapted to your level and, for 1:1, to a project you actually want to ship
1 · Agentic AI foundations
What separates an "agent" from a prompt chain. Reasoning loops, when agents help vs hurt, and how to scope an agent so it stays reliable. Where LangGraph fits against LangChain, raw SDKs, and Bedrock.
2 · LangGraph state machines
Graphs, nodes, edges, and state. Building deterministic control flow around non-deterministic models. Conditional edges, cycles, human-in-the-loop, and persistence/checkpointing.
3 · Multi-agent orchestration
Router, supervisor, and plan-and-execute patterns — and how to choose between them. Benchmarking orchestration styles on real data (drawn from a study shipped to 900+ users).
4 · Tool calling & MCP
Function/tool calling done safely, structured outputs, and exposing tools cleanly via the Model Context Protocol (MCP). Designing tools an agent can actually use without going off the rails.
5 · Memory & retrieval
Short-term vs long-term memory, session state, and grounding agents with RAG. Where retrieval belongs in an agent graph and how to keep answers cited and accurate.
6 · Guardrails & verifiers
Verifier patterns (e.g. a math/logic safety net), guardrails against hallucination, retries and fallbacks, and designing for graceful failure in front of real users.
7 · Evals & observability
LangSmith traces, offline and online evals, retrieval-precision metrics, and how to know an agent is production-ready instead of guessing. Testing non-deterministic systems.
8 · Cost, latency & deploy
Token and latency budgets, semantic caching, model selection, and packaging an agent behind an async FastAPI backend with streaming. Shipping it and watching it in production.
9 · Capstone (1:1)
For 1:1 tracks: we build or harden a real agent from your world end-to-end, so you leave with working code and the judgment to extend it.
Formats
Pick the shape that fits how you learn and what you're shipping
1:1 Private Mentoring
Fully tailored to your goals and codebase. Live, hands-on, paced to you.
- Typically 6–12 hours across several sessions
- Curriculum shaped around a real project
- Direct code review and architecture feedback
- Async follow-up between sessions
Team Workshop
Get your whole team on the same production-grade patterns, fast.
- 2–4 days, live online (in-person on request)
- Hands-on labs plus your team's real use case
- Shared vocabulary and reference architecture
- Post-workshop support window
Architecture Review
Already building an agent? Bring it and we'll pressure-test it together.
- Focused sessions on your existing design
- Orchestration, evals, cost, and reliability audit
- Concrete, prioritised recommendations
Prerequisites & Outcomes
Prerequisites
- Comfortable with Python
- Have called an LLM API at least once
- Basic REST / async understanding for advanced modules
- No prior LangGraph experience required
By the end, you can
- Design an agent graph that stays reliable under real load
- Choose the right orchestration pattern for a problem
- Add memory, tools/MCP, and RAG grounding correctly
- Put evals, guardrails, and observability in place
- Ship an agent behind a production backend
Technologies covered
- LangGraph, LangChain, LangSmith
- OpenAI & Anthropic models, function calling
- MCP, pgvector / Supabase, FastAPI
- Your stack, wherever it makes sense
Frequently Asked Questions
Do you offer 1:1 (private) agentic AI training?
Yes. Most engagements are 1:1 live online sessions tailored to your goals and codebase, alongside team workshops. Sessions are hands-on and paced to your current level.
What do I need to know before starting?
Comfort with Python and basic LLM concepts (prompts, API calls) is enough for the core track. Advanced modules assume some async Python and REST experience. No prior LangGraph experience is required.
How long does it take?
1:1 tracks typically run 6–12 hours across several sessions. Team workshops run 2–4 days. Both can be shaped around a specific project you want to ship.
Is it online or in person?
Primarily live online, available across all time zones. In-person corporate delivery is possible with advance planning.
How much does it cost?
Pricing depends on format (1:1 vs team), duration, and customization. Book a free intro call and you'll get a clear proposal for your goals.
Related Training
Go deeper on a specific part of the agent stack
LangGraph Training →
Deep-dive purely on LangGraph: graphs, state, orchestration, and persistence.
RAG Training →
Retrieval-augmented generation: chunking, embeddings, hybrid search, and evals.
MCP Training →
Model Context Protocol: build servers, expose tools, and connect them to agents.
AI Architecture Mentoring →
Senior-level guidance on designing and reviewing whole AI systems.