Build agents that can
read, decide, and act.
Practical LangChain patterns for RAG, tools, and LangGraph orchestration—templates included.
What you'll build
docs → vector DB → clean answers
Sheets, webhooks, APIs, n8n
planner → researcher → summarizer → actor with retries & fallbacks
Who it's for
Engineers and makers
who want production-credible agents
Agencies
packaging AI assistants for clients
Builders
who tried "just prompting" and hit complexity
Outcomes
Clean RAG pipeline with better retrieval & citations
Safe tool calls with allow-lists and input validation
Deployable starter that logs, retries, and degrades gracefully
Full syllabus (~2.5 hours)
Foundations
RAG vs Agents, costs/latency, when LangChain makes sense
RAG in 30 Minutes
chunking, embeddings, vector stores, re-ranking, citations
Tool-Using Agents
design safe tools (Sheets/API/webhook), error handling
LangGraph Orchestration
state, nodes/edges, planner-executor, timeouts
Evaluation & Guardrails
golden sets, toxicity/PII checks, telemetry
Shipping to Prod
serverless deploy, secrets, logging, rate limits
What you get
Starter patterns for RAG + tools + LangGraph
Evaluator prompts, guardrail templates, logging checklist
Example tool specs (Sheets, webhooks, HTTP APIs)
FAQs
Python or JS?
Both are referenced; pick what fits your stack.
Local models?
Notes for Ollama-based flows included.
Will this scale?
We cover limits, costs, and optimization paths.
Ready to build production-ready agents?
Join the waitlist and be the first to access this course when it launches.
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