AI Agents at Scale

Battle-tested frameworks, step-by-step tutorials, and real architecture patterns from running AI agents on a Mac Mini.

This is the Agent Tree Architecture — how JD Davenport runs AI agents in production across personal productivity, content creation, coding, and operations.

Start Here

New to AI agents? Follow this path (takes ~1 hour):

1. Why AI Agents Will Replace SaaS

The business case: why agents solve the mid-market revenue problem better than traditional software.

2. Agent Tree Architecture

How a CEO agent orchestrates specialist sub-agents. The operating system for agent-native companies.

3. Build Your First OpenClaw Agent

Hands-on tutorial: install OpenClaw, define your agent’s soul, give it memory, run it. 30 minutes.

Then explore deeper based on your interest:

For architects: Orchestration patterns → Delegation framework → ARD planning
For cost-conscious builders: Cost of AI agents → Smart model routing → build cheaper
For practitioners: Memory systems → Browser automation → deploy

Featured Content

The Real Cost of Running AI Agents 24/7

Actual token costs per agent type, optimization strategies, and what to expect when running agents in production.

Smart Model Routing

Route tasks to the right model (Haiku for workers, Sonnet for prose, Opus for orchestration). Significant cost reduction, zero quality loss.

Multi-Agent Orchestration

The CEO agent pattern: delegate, verify, synthesize. Why monolithic agents fail and trees succeed.

How AI Agents Remember

The 3-layer memory model: raw logs, atomic facts, curated summaries. How agents maintain context across sessions.

What’s Here

Real agent architecture running in production (coding, research, writing, monitoring, personal ops)
Significant cost reduction via smart model routing compared to running everything on Opus
Mid-market companies where agents create the most impact (complex enough to need ops, lean enough to need leverage)
Everything is free — no sign-up wall, no gatekeeping

Who This Is For

You should be here if you:

Build products/services at growing companies (or aspire to)
Want to understand how AI agents actually work in business
Are tired of manual workflows that could be automated
Care about cost optimization and realistic ROI
Like practitioner knowledge over thought leadership

You might not need this if:

You’re at a fortune 500 company (you can throw headcount at problems)
You’re pre-product (keep that spreadsheet for now)
You just want to chat with Claude (that’s a different tool)

What You’ll Learn

Frameworks

Agent Tree Architecture — The org chart for agents
Delegation Framework — When to spawn agents, which models, timeouts
ARD: Agent Requirement Document — Spec your agents before building

Articles

Why AI Agents Will Replace SaaS — The business case
Agent Orchestration — Coordination patterns
Memory Systems — Persistence and context
Cost of AI Agents — Real economics
Smart Model Routing — Cost optimization
Building in Public — The strategy behind this hub

Tutorials

Build Your First OpenClaw Agent — 30-minute hands-on
Browser Automation — Agent use case: job applications at scale

Reference

FAQ — Common questions answered
Glossary — Key terms defined

The Thesis

AI agents will have the most impact at mid-market companies where operational leverage is highest.

Large enterprises can throw headcount at problems. Tiny startups don’t have complex operations yet.

But mid-market companies are complex enough to need sophisticated operations and lean enough that every person is stretched across multiple roles.

That’s where agents create 10x leverage — not by replacing people, but by eliminating the 60% of every role that’s just moving data between systems.

About the Author

JD Davenport builds AI agent systems at OpenClaw — an open-source AI agent platform.

He’s focused on how AI agents scale operations at mid-market companies.

LinkedIn — Weekly updates on agents and building in public
GitHub — The code
GitHub: OpenClaw — The platform