+ Human Intervention is a Failure Signal
+ ++ HUMAN IN THE LOOP = BOTTLENECK
+ HUMAN IN THE LOOP = BOTTLENECK +
Think about autonomous driving. When a human has to take over the wheel, that's not a feature - it's a failure of the system. The car couldn't handle the situation on its own.
+ +Why is coding any different?
+ +When you find yourself:
+-
+
- Fixing the AI's half-finished code +
- Manually correcting obvious mistakes +
- Guiding the agent step-by-step through a task +
- Repeatedly clarifying the same requirements +
...that's not "human-AI collaboration." That's the AI failing to do its job.
+ +Oh My OpenCode is built on this premise: Human intervention during agentic work is fundamentally a wrong signal. If the system is designed correctly, the agent should complete the work without requiring you to babysit it.
+
+ Indistinguishable Code
+ +Goal: Code written by the agent should be indistinguishable from code written by a senior engineer.
+ +Not "AI-generated code that needs cleanup." Not "a good starting point." The actual, final, production-ready code.
+ +This means:
+-
+
- Following existing codebase patterns exactly +
- Proper error handling without being asked +
- Tests that actually test the right things +
- No AI slop (over-engineering, unnecessary abstractions, scope creep) +
- Comments only when they add value +
If you can tell whether a commit was made by a human or an agent, the agent has failed.
+
+ Token Cost vs. Productivity
+ +Higher token usage is acceptable if it significantly increases productivity.
+ +Using more tokens to:
+-
+
- Have multiple specialized agents research in parallel +
- Get the job done completely without human intervention +
- Verify work thoroughly before completion +
- Accumulate knowledge across tasks +
...is a worthwhile investment when it means 10x, 20x, or 100x productivity gains.
+ +However:
+ +Unnecessary token waste is not pursued. The system optimizes for:
+-
+
- Using cheaper models (Haiku, Flash) for simple tasks +
- Avoiding redundant exploration +
- Caching learnings across sessions +
- Stopping research when sufficient context is gathered +
Token efficiency matters. But not at the cost of work quality or human cognitive load.
+
+ Minimize Human Cognitive Load
+ +The human should only need to say what they want. Everything else is the agent's job.
+ +Two approaches to achieve this:
+ +Approach 1: Prometheus (Interview Mode)
+You say: "I want to add authentication."
+Prometheus:
+-
+
- Researches your codebase to understand existing patterns +
- Asks clarifying questions based on actual findings +
- Surfaces edge cases you hadn't considered +
- Documents decisions as you make them +
- Generates a complete work plan +
You provide intent. The agent provides structure.
+Approach 2: Ultrawork (Just Do It Mode)
+You say: "ulw add authentication"
+The agent:
+-
+
- Figures out the right approach +
- Researches best practices +
- Implements following conventions +
- Verifies everything works +
- Keeps going until complete +
You provide intent. The agent handles everything.
+In both cases, the human's job is to express what they want, not to manage how it gets done.
+
+ Predictable, Continuous, Delegatable
+ +The ideal agent should work like a compiler: markdown document goes in, working code comes out.
+ +Predictable
+Given the same inputs:
+-
+
- Same codebase patterns +
- Same requirements +
- Same constraints +
...the output should be consistent. Not random, not surprising, not "creative" in ways you didn't ask for.
+ +Continuous
+Work should survive interruptions:
+-
+
- Session crashes? Resume with
/start-work
+ - Need to step away? Progress is tracked +
- Multi-day project? Context is preserved +
The agent maintains state. You don't have to.
+ +Delegatable
+Just like you can assign a task to a capable team member and trust them to handle it, you should be able to delegate to the agent.
+This means:
+-
+
- Clear acceptance criteria, verified independently +
- Self-correcting behavior when something goes wrong +
- Escalation (to Oracle, to user) only when truly needed +
- Complete work, not "mostly done" +
+ The Core Loop
+ +Everything in Oh My OpenCode is designed to make this loop work:
+ +| Feature | +Purpose | +
|---|---|
| Prometheus | +Extract intent through intelligent interview | +
| Metis | +Catch ambiguities before they become bugs | +
| Momus | +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
+ +A world where:
+-
+
- Human developers focus on what to build, not how to get AI to build it +
- Code quality is independent of who (or what) wrote it +
- Complex projects are as easy as simple ones (just take longer) +
- "Prompt engineering" becomes as obsolete as "compiler debugging" +
The agent should be invisible. Not in the sense that it's hidden, but in the sense that it just works - like electricity, like running water, like the internet.
+ +You flip the switch. The light turns on. You don't think about the power grid.
+ +That's the goal.
+