Approach 1
Ultrawork
Just say "ulw" and walk away.
- Analyzes codebase context
- Breaks down task into atomic steps
- Executes implementation
- Verifies against requirements
- Commits changes
Zero intervention. Full autonomy. Just results.
Manifesto
Ultrawork Manifesto
The Philosophy of High-Output Engineering
> HUMAN IN THE LOOP = BOTTLENECK
Imagine an autonomous car that requires you to grab the steering wheel every 30 seconds. Would you call that "autonomous"? No. You'd call it driver assist — barely better than cruise control.
Why is coding any different?
We've accepted a paradigm where "AI coding" means a chatbot that writes 20 lines, then waits for you to fix it. That's not automation — that's micromanagement.
- Fixing AI's half-finished code
- Manually correcting syntax errors
- Copy-pasting context back and forth
- Reviewing every single line for hallucinations
That's not "human-AI collaboration" — that's the AI failing to do its job.
Oh My OpenAgent is built on the premise that the human should be the architect, not the spell-checker.
Indistinguishable Code
Agent-written code should be indistinguishable from code written by a senior engineer.
- Follows existing codebase patterns and architecture
- Implements proper error handling and edge cases
- Writes tests that actually test behavior, not just coverage
- No 'AI slop' — clean, concise, maintainable code
- Comments only when they add value — never stating the obvious
"If you can tell whether a commit was made by a human or an agent, the agent has failed."
Token Cost vs. Productivity
We don't care about token usage. We care about output. If spending $5 on tokens saves an hour of engineering time, that's a 20x ROI.
- Parallel agents exploring multiple solutions
- Complete work without human intervention
- Thorough self-verification loops
However...
We optimize for efficiency where it counts. Not by crippling the model, but by:
- Using cheaper models for routine tasks
- Avoiding redundant exploration
- Intelligent caching of context
- Stopping exactly when sufficient
Minimize Human Cognitive Load
The human should only need to say what they want. Everything else is the agent's job.
Approach 2
Planner + Executor
When you want strategic control.
Ultrawork Planner
Runs the interview, researches context, and writes a detailed work plan.
/ulw-execute
Executes the approved plan in the same session, delegates by category, manages waves, and tracks progress.
You architect. Agents execute. Full transparency.
Predictable
Same inputs = consistent output. No random deviations or creative liberties unless requested.
Continuous
Survives interruptions. Tracks progress in real-time. Preserves context across sessions.
Delegatable
Clear acceptance criteria. Self-correcting mechanisms. Escalation only when absolutely needed.
The Core Loop
- Human Intent
- Agent Execution
- Verified Result
Planning interview
Extract intent through intelligent interview
Plan consultant
Catch ambiguities before they become bugs
Plan review gate
Verify plans are complete before execution
Orchestrator
Coordinate work without human micromanagement
Todo Continuation
Force completion, prevent "I'm done" lies
Category System
Route to optimal model without human decision
Background Agents
Parallel research without blocking user
Wisdom Accumulation
Learn from work, don't repeat mistakes
The Future We're Building
- Human developers focus on WHAT to build, not HOW to get AI to build it
- Code quality independent of who wrote it
- Complex projects as easy as simple ones
- "Prompt engineering" becomes obsolete
"The agent should be invisible. Like electricity, like running water."
"You flip the switch. The light turns on. You don't think about the power grid."