AI & Machine Learning💻 Technical CourseLearnAspire Certified

Agentic AI in Production: Stateful Multi-Agent Systems with LangGraph 0.2 and MCP

“Harden the multi-agent system you already built.”

Build, instrument, and ship a checkpoint-resilient multi-agent pipeline with MCP tool servers and human-in-the-loop gates

Advanced11h6 modules37 slides18 exercises24 quiz Qs
🔥 Launch Price — 63% off. Limited time.
₹2,999₹7,999

One-time · Lifetime access · Certificate included

Sign in to Enroll
7-day money-back guarantee
  • ✓6 modules of content
  • ✓37 concept slides
  • ✓18 practical exercises
  • ✓24 quiz questions
  • ✓Capstone project
  • ✓LearnAspire certificate

Learning Outcomes

What you'll learn

→Replace MemorySaver with PostgresSaver and configure a ConnectionPool that survives 25 concurrent workers without silent queuing
→Design custom state reducers (Annotated[list, operator.add]) for fields that need append semantics, and migrate TypedDict schemas without breaking existing checkpoints
→Wire an interrupt() gate for human-in-the-loop approval that resumes from the correct node after an external response — no polling loops, no lost state
→Build and expose a Model Context Protocol (MCP) tool server in Python so one tool implementation serves every agent in your organization
→Split a monolithic agent into supervisor-worker handoffs where each specialist sub-agent is independently evaluable and the handoff logic is explicit in code
→Instrument a multi-agent workflow with LangSmith tracing and structured JSON logs that let you debug a failed run in 90 seconds, not two hours
→Complete a 5-gate Production Readiness Scorecard with trace IDs, query results, and log lines as evidence — and ship to production with your name on the runbook

The day after you finish

You can open a LangSmith project URL showing a LangGraph 0.2 supervisor delegating to two sub-agents with named trace children, walk into a staff-engineer design review with a runnable Docker Compose stack containing PostgresSaver-backed state and a registered MCP tool server, and kill the orchestrator container mid-run to demonstrate that an in-flight case resumes from the exact checkpoint where it paused — without re-executing prior nodes, losing tool-call results, or breaking the audit trail.

Who this is for

  • Primary: AI engineers and senior Python developers who have a LangGraph prototype running in dev and now need to ship it — where 'ship' means survive process crashes, pass compliance review, and get paged correctly when something breaks.
  • Secondary: Platform engineers and SREs being brought in to fix a flaky agent system they didn't build, who need a production mental model for stateful LLM workflows — checkpointing, observability, failure modes.
  • Tertiary: Tech leads and engineering managers evaluating whether their team's LangGraph work will scale past the demo, or whether to rebuild around Temporal or Airflow.

Prerequisites

  • You've built at least one LangGraph agent (even a prototype) and understand StateGraph, nodes, conditional edges, and how state is passed between them
  • Working Python 3.11+ environment, comfort with async/await, and basic Postgres — you can run psql and read a query result
  • You've deployed at least one Python service to production (Docker, Lambda, Fly, Vercel, or equivalent) and understand what 'process crash' and 'concurrent requests' mean in practice

Curriculum

6 modules · full breakdown

🤖 Part of: AI Engineering Path

Step 1 — Foundations
→Step 2 — Core Skills
→Step 3 — RAG
→Step 4 — LangGraph RAG
→Step 5 — Agent Systems
→Step 6 — Production
→Step 7 — MCP
→Step 8 — Enterprise
← Previous: Step 5 — Agent SystemsNext in path: Step 7 — MCP →
🏆

Capstone Project

Ship the Vektora Labs Triage Mesh to Production

Vektora Labs is a simulated healthcare incident-triage platform. Their current agent system — a supervisor routing triage cases across three specialist workers (Clinical, Billing, Compliance) — runs fine in staging but has never passed production readiness review. You inherit the codebase, apply every technique from Modules 1–5, and prepare the system for shipping: PostgresSaver with correct ConnectionPool sizing, interrupt() gates on high-severity cases, MCP tool server for internal systems, supervisor-worker handoffs with explicit state contracts, full LangSmith tracing, structured logging across every node. Then you produce the 5-gate Production Readiness Scorecard — documenting what's ready, what's risky, and what's blocking — with evidence (trace IDs, query results, log lines) behind every claim.

What you'll deliver

A completed Production Readiness Scorecard (Markdown + evidence table) for the Vektora Labs Triage Mesh, showing 5 gates with PASS/FAIL status, concrete evidence references (LangSmith trace IDs, Postgres query results, log lines), and remediation plans for any FAIL gates. Attached: the GitHub repo of the hardened triage mesh (LangGraph 0.2 + PostgresSaver + MCP + LangSmith), runnable end-to-end.

Portfolio value

The Production Readiness Scorecard is a real artifact you can send to your CTO, hiring manager, or a new team's tech lead — a full walkthrough of how you harden a multi-agent LangGraph system for production, with evidence for every claim. This is what 'Advanced LangGraph' actually looks like on a resume.

Sign in to Enroll →