Agentic AI: LangChain to Production LangGraph
From AI Tutorials to Fault-Tolerant Agent Systems
Build fault-tolerant agent systems with state, human oversight, and production observability.
The AI landscape has shifted. Building an AI application is no longer the hard part — shipping one that is reliable, observable, cost-aware, and maintainable in production is. Most engineers today can call an LLM API. Very few can design a fault-tolerant agent system with durable state, human oversight controls, structured fallback logic, and a hardened deployment pipeline.
This cohort moves from LangChain fundamentals to designing and deploying production-grade agentic systems using LangGraph 1.0. Every module is project-first. Every deliverable is portfolio-ready. Observability is active from Day 1 — students trace every LLM call, measure every retrieval, and test every graph before wiring it to enterprise tooling.
The AI landscape has shifted. Building an AI application is no longer the hard part — shipping one that is reliable, observable, cost-aware, and maintainable in production is.
Most engineers today can call an LLM API. Very few can design a fault-tolerant agent system with durable state, human oversight controls, structured fallback logic, and a hardened deployment pipeline. Prompt engineering got us here, but flow engineering is what ships reliable AI products.
This course is structured around that conviction. Every module is project-first, every deliverable is portfolio-ready, and observability is active from Day 1.
Before
"I prompted my way to a working demo. When it fails, I'm not sure why — and I have no way to measure improvement."
After
"I design stateful, cyclic agent graphs with checkpoints, fallback edges, and human oversight. Every LLM call is traced. Every retrieval is measured."
- 1Treats hallucination as a first-class failure mode with compensating architecture, not bad luck
- 2Ten+ deployed agent systems — from a validated provider-aware chatbot to a fault-tolerant financial agent with checkpoint recovery
- 3A production API layer with SSE streaming, auth, rate limiting, and full observability tracing active from day one
- 4Architectural judgment: when to use LangGraph vs. LCEL, when to add a human-in-the-loop gate, which model for which task
50.5h · 10 modules across 4pillars · every session ends with a working, reviewable artifact. Expand any pillar for the session lineup.
Foundations & RAGToolchain, LangChain, RAG with observability · Module 1–2▼
- Module 1 — LLM Foundations, Toolchain & Model Strategy
- Module 2 — RAG Fundamentals with Observability
LangGraph & Agentic RAGState graphs, CRAG, ReAct, Reflexion · Module 3–4▼
- Module 3 — Graph Theory & LangGraph Core
- Module 4 — Agentic RAG & Advanced Reasoning Architectures
Production EngineeringState persistence, APIs, multi-agent, testing · Module 5–8▼
- Module 5 — Production-Grade State, Persistence & Fault Tolerance
- Module 6 — Production API Layer: FastAPI, Streaming & Docker
- Module 7 — Multi-Agent Systems & Frontend Integration
- Module 8 — Testing, Evaluation & Observability
Enterprise & Capstonen8n integration, final project · Module 9–10▼
- Module 9 — Enterprise Orchestration with n8n
- Module 10 — Capstone Project
Want the full session-by-session syllabus with objectives and deliverables? Request it here— we'll send it over.
| Delivery | Cohort-based, live, instructor-led |
| Class length | 2 hours per class |
| Cadence | Live online, cohort-based — weekly sessions |
| Total length | 50.5h · 10 modules over 10 weeks |
| Location | Dhaka + Global (remote seats available) |
| Prerequisites | Python proficiency, Git, REST API basics. AI Engineering (Course 4) recommended. |
৳4,500
$69 global
Coming soonThis course launches soon. Join the waitlist for early access and launch-day pricing.
Do I need LangChain experience?▼
No. Module 1 starts from LangChain fundamentals. You need Python proficiency.
How is this different from AI Engineering (Course 4)?▼
Course 4 builds reliable single-model features. This course composes models into autonomous, stateful agent systems with LangGraph.
What's the capstone?▼
A demonstrable, portfolio-ready AI system with a LangGraph multi-node graph, HITL interrupt, FastAPI streaming endpoint, and LangSmith evaluation suite.
Part of a longer track?
This course is part of the TIER B · ENGINEERING. Explore the full catalog to find related courses that build on each other.
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