For automation engineers and AI developers ready to build multi-agent coordination systems.
Track 1 taught you to USE agentic AI safely. Track 2 teaches you to BUILD industrial-grade multi-agent systems with parallel validation, event-driven coordination, and fault-intelligent behavior.
You'll learn LangGraph state machines, multi-agent debate patterns, parallel worker swarms, and how to orchestrate agents that validate each other's outputs before any PLC code touches hardware.
31% complete
Complete all 16 tutorials to build production-grade multi-agent systems. Start with D1 for Python foundations needed in this track.
TypedDict, async/await, logging, and safe failure modes
Use TypedDict, async/await, logging, and error handling to make industrial AI agents reliable with PLC and alarm data.
async/await, retry, backoff, and safe failure modes
Deepen your async, retry, and backoff strategies so agent workers stay safe and predictable under real plant conditions.
ChatModels, prompt templates, output parsers, and runnable chains
Build composable LLM workflows for industrial agents using LangChain: ChatModels, prompt templates, output parsers, and runnable chains.
with_structured_output(), nested BaseModel, and typed agent contracts
Design production-grade schemas with with_structured_output(), nested Pydantic models, and validation patterns that become the typed contracts LangGraph state and tool returns depend on.
Build stateful agent graphs with nodes, edges, and cycles
Master StateGraph to build stateful agent workflows with typed state, conditional edges, and cyclic reasoning patterns.
Model Context Protocol for industrial tool integration
Learn MCP concepts with a read-only tool server, shared tooling, and security boundaries for industrial agents.
Embeddings, Vector Stores, and Citation-Ready Retrieval
Build citation-ready RAG with LlamaIndex: ingest docs, embed & store vectors, retrieve evidence, and log citations for audits.
Line-scoped persistence across shifts
Design line-scoped agent memory with LangGraph checkpointing plus persistence (SQLite/Redis) for 24/7 industrial shift handovers.
Shared tool layer for multi-agent systems
Wrap your plant data sources and services behind MCP servers so multiple agents can share reliable tools.
Coordinator-worker with shared MCP servers
Design multi-agent coordinator–worker systems sharing MCP servers for industrial diagnostics.
Multi-agent voting patterns for industrial validation
Build agent swarms with weighted consensus voting, health checks, and majority thresholds for PLC code validation.
Citation-ready retrieval with vendor filtering
Build vendor-specific RAG for PLC documentation with citation tracking and metadata filtering for multi-vendor systems.
Streaming processing, anomaly detection, and advisory-only recommendations
Build shadow-mode diagnostic loop from live PLC tags: stream processing, anomaly detection, advisory recommendations without PLC writes.
Seasonality, similar-episode retrieval, and predictive maintenance signals
Turn SCADA alarm history into seasonal baselines, similar-episode retrieval, and predictive maintenance signals (advisory-only).
Zero-Cost Testing with Historian Replay, Physics Checks, and Shadow Mode
Test agent recommendations at $0 using historian replay, constraint gates, physics checks, mock OPC UA tags, and shadow mode logging.
Assemble memory, tools, coordination, RAG, live signals, and validation into one end-to-end system
Build one integrated diagnostic system combining agent memory, MCP tools, multi-agent coordination, RAG context, live and historical signals, and validation layers.
Begin with D1 for the Python foundations you need, or jump to D2 if you're already comfortable with async patterns and TypedDict.