Running a Website with a Fleet of AI Agents
Most AI agent tutorials are hypothetical. This course is not. We run a production website using a fleet of autonomous AI agents - one that drafts course content every Thursday, one that fixes accessibility bugs, one that watches for feature requests, and more. This is the first-hand case study: the patterns that make it work, the patterns that failed, and the playbook for building your own fleet.
Course Lessons
From the fleet architecture to the failure stories - follow in order or jump to any topic.
1. The Fleet Model
Why we run a website with autonomous AI agents, what the fleet looks like, and the principles that separate working fleets from chaotic ones.
2. Specs as Programs
Natural-language specification files are the operating system for each AI agent. How to write them so agents do exactly what you mean.
3. The TODO-as-Blackboard Pattern
Multi-agent coordination without message queues. How a shared TODO file becomes the fleet’s work queue, audit log, and state machine.
4. Tool Use and Capability Design
Which tools each agent type needs, the principle of least privilege applied to AI, and how to design tool grants that make catastrophic mistakes hard.
5. Guardrails and Human-in-the-Loop Gates
Why fully autonomous agents are dangerous in production. The PR gate pattern, scope guards, and where humans must stay in the loop.
6. Monitoring and the Metrics Feedback Loop
Measuring agent outputs, not just agent runs. The three metrics layers, the signal→diagnosis→action loop, and how metrics drive the next spec iteration.
7. Failure Modes and Recovery
Real failures from the logs: the 4.5-hour zombie deploy, scope creep incidents, hallucinated file links, and the recovery patterns that fixed them.
8. Build Your Own Fleet: The Playbook
The fleet maturity model, a 30-day stand-up plan, the fleet design checklist, and the compounding advantage that makes the approach defensible over time.
What You Will Learn
By the end of this course, you will be able to:
Write Specs That Work
Translate repeatable tasks into unambiguous specification files that AI agents can follow reliably - and that fail safely when they can’t.
Coordinate Multiple Agents
Use the TODO-as-blackboard pattern to let agents share state, claim work, and hand off results - without message queues or coordination infrastructure.
Build Safe Guardrails
Design the PR gates, scope constraints, and human review checkpoints that keep autonomous agents from causing harm in production.
Close the Feedback Loop
Measure agent outputs at the goal level, diagnose what’s working and what isn’t, and use those signals to improve specs over time.
Go Deeper: Companion Courses
This is the fleet architecture layer. These courses go deep on the agent patterns and production operations it relies on.
AI Agent Frameworks in Practice
LangGraph, CrewAI, and OpenAI Agents SDK compared - the code-level view of what our fleet agents are built on.
Production Readiness Runbook for LLM Systems
The 40-point pre-deploy checklist, monitoring patterns, incident response, and rollback strategies for LLM systems - the operations layer this fleet relies on.
Prompt Patterns That Survive Production
The specification patterns inside each agent: role anchoring, output contracts, and the failure-mode checklist that makes agent behavior reliable.
Token Optimization
Agents in a fleet burn tokens continuously. The cost discipline that keeps a multi-agent system economically viable over months.
AI Agents
Agent architecture fundamentals: reasoning loops, tool use, memory patterns, and multi-agent coordination before you build the fleet.
AI Workflow Automation
Broader workflow automation patterns - when agents are the right tool and when simpler automation serves better.
Forward Deployed AI Engineer
The role that deploys agent fleets at customer sites - discovery, prototyping, stakeholder management, and knowledge transfer so the customer can run the fleet after you leave.
Go Deeper With Expert Courses
Recommended learning resources from our partners. Affiliate disclosure.
DataCamp - AI & Data Science
Hands-on Python, machine learning, and AI courses with interactive exercises and real projects. Track-based learning for practitioners.
DataCampedX - Top AI Courses
Courses and MicroMasters from MIT, Harvard, Stanford, and other top universities. Earn certificates that employers recognize.
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