Asleep At The Loop

@GeoffreyHuntley's awesome Ralph Wiggum went viral recently. Bash loop, PRD, let the agent run overnight.

I love this approach. I made Taskmaster for the same reasons. The desire to let agents iterate autonomously, working through tasks while you focus elsewhere. The appeal is real: maximum leverage, continuous progress, code shipping while you sleep.

I've also learned where it breaks down, and more importantly, where it shines.

The timing of Ralph Wiggum's popularity isn't accidental. Context windows are bigger. Agents are more capable. The temptation to let them run longer, with less supervision, keeps increasing. This is the logical endpoint: maximum autonomy, minimum involvement.

The mechanism is elegant: agent picks a task from PRD.json, implements it, runs tests, marks it complete, moves to the next. Progress.txt tracks what happened. The loop continues until everything's done or something breaks.

For exploration, this is genuinely powerful.

When you're searching for the right approach and don't know what you want yet. When you need to see twenty different implementations to understand which direction is right. When the goal is divergence, not convergence.

The iteration count is the point. Ralph Wiggum might try forty different approaches before landing on something that passes tests. For shipping to production, that sounds like waste. For discovery, that's the feature. You want the agent exploring solution space while you sleep. You want to wake up to options you wouldn't have thought of.

This is why we've been streamlining Taskmaster's dependency graph over the last few months to power exactly this workflow. Autonomous iteration accelerates discovery dramatically. That's the genuine value there.

But that's also one of the reasons we refocused most of our attention.

The trap isn't the approach itself. It's when discovery mode becomes production mode.

In production, those fast iterations aren't generating options to evaluate. They're shipping.

Each time through the loop, the agent encounters ambiguity. The PRD says "handle errors gracefully" but doesn't specify what gracefully means. The task says "add user settings" but doesn't define which settings or how they persist. The spec says "integrate with the API" but doesn't cover what happens when the API times out.

The agent doesn't pause. It decides. Modal or toast? Local storage or database? Retry or fail? It picks based on patterns in its training data, based on whatever context fits in the window. The loop continues.

By morning, you have PRs. Each one technically works. Each one contains decisions nobody made.

This industrialized abdication is powered by the design decisions agents make when you're not watching. Ralph Wiggum in production mode is that pattern at scale. A loop optimized for making decisions without you.

And it inverts something I keep coming back to: context gets captured by being where decisions happen. Ralph Wiggum is designed around not being there. Progress.txt tracks what the agent did. It doesn't track why it chose one approach over another. The reasoning evaporates even faster than it does in Slack threads. At least Slack threads you could search.

The tests pass because the tests check behavior, not intent. CI stays green because green means "doesn't crash," not "does what we needed." The PRD is complete. The product is subtly off in ways you won't notice until you try to build the next thing on top of it.

This isn't a problem with agents being bad at coding. They're not. The code quality from a well-structured loop can be surprisingly good. The architecture can be clean. The patterns can be consistent and enforced.

The consistency just comes from training data rather than your product vision. The agent is consistent with itself. It's not necessarily consistent with the decisions your team made last quarter/week, or the constraints your customers care about, or the direction you're trying to move.

This matters more on teams

Solo dev on a side project? You generated it, you don't fully understand it, but you're the only one who needs to work with it. The tradeoff might be worth it.

On a team, it compounds. Someone else needs to review those PRs. Someone else needs to build on top of that code. Someone else needs to debug it when something breaks. The decisions buried in overnight runs become questions for everyone who touches the codebase later.

The coordination cost of code nobody fully understands adds up. Features built on top of mystery decisions require archaeology. The velocity gained overnight gets spent in alignment conversations, debugging sessions, and refactors when hidden assumptions finally surface.

What it looks like when it works

So what does this look like when it works for both discovery and production?

The loop still runs. The iteration speed stays high. But there's a fork: discovery mode and production mode.

Discovery mode: let it run loose. Generate options. Explore the space. The output is raw material for decisions, not the decisions themselves. You evaluate what emerged and choose a direction.

Production mode: the loop has checkpoints. When the agent encounters a design decision (not an implementation detail, but an actual choice about how the product should behave) it surfaces the options. A human makes the call. Then the loop continues.

This is captured discovery applied to autonomous agents — it's core to what we call the AI-native SDLC. The agent still iterates fast. The mechanical work still happens at machine speed. But decisions that matter get made explicitly rather than by default.

The question isn't whether to use autonomous loops. It's recognizing which mode you're in.

Implementation decisions (which syntax, which library, which pattern for this common problem): let the loop handle it. Agents are good at this and the cost of a wrong choice is low.

Design decisions (how should this behave, what tradeoffs are we making, what assumptions are we encoding): these need to surface. The cost of a wrong choice compounds every time you build on top of it.

Ralph Wiggum treats all decisions the same: make them fast, keep the loop moving. That's what makes it awesome for discovery. That's what makes it a trap for production.

The approach isn't wrong. Using it for everything is.

Discovery: let it run. Generate options. Throw most of it away. Wake up to possibilities.

Production: The loop is the same. The question is whether you're exploring or shipping. One generates options. The other generates decisions.

Know which one you're in before you go to sleep.