The Problem
I was working 14-hour days. Building products, managing clients, writing code, answering emails. And still feeling behind.
The fundamental problem: I was the bottleneck.
Every task required my attention. Even tasks that didn't need my judgment. Research. Formatting. Documentation. Scheduling.
Then I discovered OpenClaw in January 2026. It was released in November 2025, and I'd been following the project since early beta.
What is OpenClaw?
OpenClaw is an autonomous AI agent that runs on your machine 24/7. Think of it as a digital employee that:
- Reads your files and context
- Controls your browser
- Executes code
- Sends messages
- Schedules tasks
- Learns your preferences
According to OpenClaw's documentation, it's:
"A 24/7 autonomous AI teammate that works while you sleep."
The Competition
The autonomous agent space is heating up:
| Tool | Creator | Strength | |------|---------|----------| | OpenClaw | Open source | Most features, active development | | Antfarm | snarktank | Workflow automation, multi-agent | | Claude Code | Anthropic | CLI-focused, local-first |
I've been following all of them. Antfarm (from snarktank) is particularly interesting for workflow automation.
The Architecture
How It Works
┌─────────────────────────────────────────┐
│ OpenClaw Core │
├─────────────────────────────────────────┤
│ ┌─────────┐ ┌─────────┐ ┌────────┐ │
│ │Memory │ │Skills │ │Tools │ │
│ │System │ │(MCP) │ │ │ │
│ └─────────┘ └─────────┘ └────────┘ │
├─────────────────────────────────────────┤
│ ┌─────────────────────────────────────┐ │
│ │ Context (Goals, Preferences) │ │
│ └─────────────────────────────────────┘ │
├─────────────────────────────────────────┤
│ ┌─────────────────────────────────────┐ │
│ │ LLM (Claude, GPT, MiniMax) │ │
│ └─────────────────────────────────────┘ │
└─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ Your Computer │
│ Browser, Terminal, Files, APIs │
└─────────────────────────────────────────┘The Memory System
This is what makes OpenClaw different from ChatGPT:
- AGENTS.md — Your personal instructions
- MEMORY.md — Long-term memory
- Daily logs — Session history
- File context — What it's working on
The agent knows:
- Your goals
- Your preferences
- What you've worked on before
- How you like to be communicated with
Day 1-7: The Learning Phase
What I Did
I spent the first week brain dumping everything about me:
I want you to know:
- I'm a product manager turned developer
- I run an agency (clawcraft.agency)
- I value MVP over perfection
- I hate scope creep
- I want to post content daily
- I gym 3x per week
- I care about sleep
What It Learned
By day 7, the agent could:
- Reference my goals without prompting
- Know my communication preferences
- Understand my business context
Day 8-20: The Productivity Explosion
What It Started Doing
- Research — "I researched 5 AI agent tools and here's the comparison"
- Code — "I built a landing page for your agency site"
- Content — "I drafted 3 tweet ideas based on today's news"
- Planning — "Here's tomorrow's priority list"
Real Examples
Example 1: The Competitor Analysis
I asked: "Who are my competitors for AI agency services?"
It:
- Searched Google
- Found 5 competitors
- Analyzed their positioning
- Created a comparison table
- Suggested differentiation strategies
Time saved: 4 hours
Example 2: The Blog Post
I asked: "Write a blog post about my journey"
It:
- Read my AGENTS.md
- Researched my background
- Wrote 1500 words
- Suggested a title
- Created an outline
Time saved: 3 hours
Day 21-30: The Autonomy Level
The Morning Brief
I set up a cron job:
Every morning at 8am, send me a brief with:
- Tasks completed overnight
- Today's priorities
- Relevant news
- Suggestions for what to focus on
Now I wake up to a ready-made day.
Antfarm Integration
I've also been experimenting with Antfarm (from snarktank):
"Multi-agent workflow orchestration for OpenClaw"
It's like having a team of agents instead of one:
- Research agent — Gathers info
- Code agent — Writes code
- Content agent — Drafts posts
- PM agent — Coordinates everything
This is the multi-agent future I wrote about.
The Mission Control
One of the coolest things about this ecosystem is the Mission Control pattern:
- OpenClaw Mission Control — Dashboard for managing agents
- My custom setup — My personalized version
Think of it as a control tower for your AI workforce.
Harness Engineering: The Missing Framework
This is where it gets interesting. I recently discovered OpenAI's Harness Engineering concept:
"Engineering AI agents to work reliably in production"
The key insight: It's not about the agent, it's about the system around it.
As Ryan Carson (@ryancarson) wrote about Code Factory:
The future of software isn't about writing code — it's about orchestrating agents to write code for you.
The Framework I'm Building
Based on these concepts, here's my harness:
┌─────────────────────────────────────────┐
│ Harness Layer │
├─────────────────────────────────────────┤
│ ┌──────────┐ ┌──────────┐ │
│ │ Intent │ │ Context │ │
│ │ Parser │ │ Manager │ │
│ └──────────┘ └──────────┘ │
│ ┌──────────┐ ┌──────────┐ │
│ │ Output │ │ Feedback │ │
│ │ Validator│ │ Loop │ │
│ └──────────┘ └──────────┘ │
└─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ Agent Layer │
└─────────────────────────────────────────┘The Metrics
30 Days By Numbers
- Hours saved: ~80+
- Tasks delegated: 150+
- Code files created: 50+
- Blog drafts: 5
- Research reports: 8+
Productivity Increase
I'd estimate 2x productivity on repetitive tasks.
The Challenges
1. Context Windows
LLMs have limited context. I learned to:
- Break big tasks into smaller ones
- Use external files for reference
- Summarize before continuing
2. Hallucinations
Sometimes it confidently says wrong things. Now I:
- Ask for citations
- Verify before shipping
- Use tools that provide sources
3. Security
It has access to everything. I:
- Use separate credentials for risky operations
- Review before public posts
- Don't give it API keys directly
Conclusion
30 days with OpenClaw changed how I think about work.
The question isn't "can AI do this?" It's "should AI do this?"
The answer: if it doesn't require your judgment, delegate it.