Murray Marshals: Command Your AI Agent Swarm

Learn what makes an AI agent different from a chatbot. Understand goals, tools, memory, and decision loops. Identify low-risk automation candidates in your business and design your first AI agent workflow with clear boundaries and human oversight.

This module teaches you how to design AI workflows that behave like dependable crew members—not chaos gremlins. You’ll learn prompt patterns that produce consistent outputs, how to connect models to tools (tool/function calling), how to enforce structured JSON outputs, and how to build evaluations and monitoring so your workflows improve over time. You’ll leave with a complete “Workflow Spec” you can implement in n8n or your app: inputs, prompts, tools, schemas, fallbacks, tests, and weekly review.

RAG is how your AI stops improvising and starts bringing receipts. In this module you’ll design a reliable Retrieval-Augmented Generation system: ingest sources, chunk intelligently, embed and store, retrieve and rerank, generate answers with citations, and validate with an evaluation loop. You’ll leave with a full RAG spec and an eval plan you can actually run—plus guardrails for freshness, hallucination risk, and prompt injection.

RAG gives your AI facts. Tools give it hands. Security gives you trust. In this module, you’ll design tool integrations that are safe by default: strict JSON schemas, least-privilege scopes, OAuth best practices, idempotency keys, rate limits, and monitoring. You’ll create a Tool Catalog entry (schema + controls) and a deployment-ready checklist so your AI workflows can act without becoming a liability.

Automation is choreography: the right step, at the right time, with the right fail-safes. In this module you’ll learn orchestration patterns that make workflows resilient—retries with backoff, timeouts, dead-letter/error routes, human approvals, and runbooks. You’ll design a complete Lead → Demo → Proposal → Invoice workflow and produce a production-ready orchestration spec you can implement in n8n or Make without turning your business into a fragile Rube Goldberg machine.

If you can’t debug it, you can’t ship it. This module turns your AI system into something you can trust in production: evaluation datasets, measurable thresholds, tracing and monitoring signals, validator gates, red-team tests (prompt injection), and regression alerts that prevent silent quality decay. You’ll build an Eval Plan + Observability Spec + Alert Playbook that makes your AI workflows boring, reliable, and safe to scale.

This is the final montage. You’re not learning another concept—you’re shipping a working system. You’ll assemble the components you’ve built across the course into a coherent AI-first operating system: a RAG knowledge layer, tool catalog with secure controls, orchestration flows, evaluation + observability guardrails, and cost/latency routing. You’ll publish a demo link, document your system cards, define KPIs, and create a go-live checklist that prevents the classic “it worked in dev” disaster.