Wigging Out: Controlled Autonomous Loops in Zenflow

There's a technique making the rounds in the AI coding community called "Ralph Wiggum." Named after the Simpsons character who just keeps going, blissfully unaware, it's exactly what it sounds like: let your AI agent run in a loop until the task is done. And while the result is certainly a paradigm shift from the conversational approach to agents many of us are still used to, the Wiggum technique’s structureless, almost Darwinian approach to iteration can quickly go off the rails without a guiding hand.

The implementation is almost comically simple. Geoffrey Huntley, who coined the term, described it as:

while :; do cat PROMPT.md | claude ; done

That's it. Feed the agent a prompt, let it work, and when it stops, feed it the same prompt again. Each iteration sees the modified files and git history from the previous run. The agent iterates toward completion, failing forward until it succeeds.

The results can be impressive. Huntley ran a loop for three consecutive months that produced Cursed, a functional programming language with Gen Z slang keywords (slay for function, sus for variable, based for true). At a Y Combinator hackathon, teams using the technique shipped multiple repositories overnight for under $300 in API costs.

The philosophy inverts typical AI coding workflows. Instead of reviewing each step, you define success criteria upfront and let the agent iterate toward them. As Huntley puts it: "Better to fail predictably than succeed unpredictably."

The Problem with Raw Loops

The Ralph Wiggum technique works great until it doesn't; the failure modes are well documented. A 50-iteration loop on a large codebase can burn $50-100+ in API credits, and that’s before considering the real-world quality and technical constraints of technical infrastructure.. Agents drift from the original intent over many iterations, what you might call "divergence." And without visibility into what's happening, you're left juggling terminal windows hoping for the best.

The community has built wrappers to address this. Tools like ralphio add memory systems and TDD support. Others add rate limiting and circuit breakers. But these all require bash scripting, tmux dashboards, and manual safety controls.

The technique also has clear boundaries. Don't use it for architectural decisions, security-sensitive code, or ambiguous requirements. It works best for mechanical tasks with well-defined completion criteria: migrations, batch refactors, test coverage. The skill shifts from "directing the agent step by step" to "writing prompts that converge." But what if you could have both? The autonomous iteration of Ralph Wiggum with the structure to prevent the failure modes?

Why Specs Matter

The core problem with raw loops is that there's no anchor. The agent has a prompt, but prompts are interpreted fresh each iteration. Over dozens of cycles, small misinterpretations compound. The agent "drifts" from what you actually wanted.

This is why spec-driven development has become central to reliable AI coding. The idea is simple: before the agent writes code, it produces a specification. The spec captures requirements, acceptance criteria, and constraints. Implementation follows the spec, not the original prompt.

The spec becomes the anchor. When the agent completes a step and looks for what to do next, it references the spec, not its memory of your initial request. Errors get caught at the spec level before they propagate into code. And because the spec is a document you can read and edit, you maintain visibility into what the agent thinks it's building.

Spec-driven workflows also make review practical. Reviewing a short spec is faster than reviewing a 20-file diff. If you catch a misunderstanding early, you save tokens and time. You stay in control of the codebase instead of rubber-stamping AI output you don't fully understand.The question is how to combine this with autonomous iteration. You want the agent to keep working without babysitting, but you also want it anchored to a defined process.

The Zenflow Approach

Zenflow's workflows are defined in markdown files, specifically in plan.md. When you create a task, you can edit this file directly before running. The agent follows the steps you define, and here's the critical part: the agent can add new steps to plan.md as it works.

This creates the loop. But unlike a raw bash loop, you have:

• Visibility: Zenflow's UI shows what every agent is doing across your project
• Control: Toggle "Auto-start steps" for continuous execution, or pause for review between steps
• Structure: The workflow definition itself sets behavioral guardrails
• Portability: Save workflows and reuse them across projects

The loop mechanism lives inside a spec-driven workflow rather than outside it. The agent isn't just iterating blindly. It's following a defined process that it can extend. The plan.md file serves as both the spec and the execution log, so you always know what the agent is doing and why.

How It Works

Zenflow ships with built-in iterative workflows for common tasks: new features, bug fixes, and migration planning. These already incorporate the loop-and-verify pattern that makes autonomous execution reliable. But you can also build your own:

---
# Quick change

## Configuration
- *Artifacts Path*: {@artifacts_path} → `.zenflow/tasks/{task_id}`

---

## Agent Instructions

This is a quick change workflow for small or straightforward tasks 
where all requirements are clear from the task description.

### Your Approach
1. Proceed directly with implementation
2. Make reasonable assumptions when details are unclear
3. Do not ask clarifying questions unless absolutely blocked
4. Focus on getting the task done efficiently

If blocked or uncertain on a critical decision, ask the user for direction.

---

## Workflow Steps

### [ ] Step: Implementation

Implement the task directly based on the task description.

1. Make reasonable assumptions for any unclear details
2. Implement the required changes in the codebase
3. Add and run relevant tests and linters if applicable
4. Perform basic manual verification if applicable

Save a brief summary of what was done to `{@artifacts_path}/report.md` 
if significant changes were made.

After you are done with the step add another one to 
`{@artifacts_path}/plan.md` that will describe the next improvement opportunity.

The last line is the loop: "After you are done with the step add another one to plan.md." The agent completes a step, identifies the next improvement, adds it to the plan, and continues.

To set this up:

1. Create a new task with your prompt
2. Hit "Create" (not "Create and Run")
3. Click "Edit steps in plan.md" and paste or modify the workflow
4. Toggle "Auto-start steps" to keep it running continuously
5. Save it as a custom workflow if you want to reuse it

The Refined Pattern

Raw Ralph Wiggum loops have no built-in review. The agent implements, then implements more, then implements more, compounding any errors along the way.

But let’s consider a simple yet still more structured flow: Implement → Review → Fix, limited to 3 iterations.

This is the "Goldilocks zone" that Zencoder's research identified. Heavy multi-step processes often multiply errors rather than fix them. Massive prompt templates fail in practice. The most reliable setups have just enough structure without over-orchestration.

The refined pattern adds a review step between iterations. This catches drift before it compounds. You can configure the reviewer to be a different model (Claude checking GPT's work, or vice versa) which catches blind spots neither would find alone. Zencoder calls this the "committee approach," and internal testing showed it produces quality improvements comparable to waiting for the next model generation.

The 3-loop limit prevents runaway costs and divergence. If the task isn't converging after three cycles of implement-review-fix, it probably needs human intervention anyway. This is the spec-driven philosophy at work: structure that enables autonomy rather than fighting it.

Orchestration Over Prompting

Zenflow retains core continuous iteration but adds the structure that raw loops lack. The workflow lives in a spec: editable, visible, reusable. The agent extends the plan rather than blindly re-running. Review gates and loop limits prevent the failure modes.

This is what spec-driven development looks like in practice. Anchor the agent to a defined process, let it execute within those bounds, verify before shipping. The result is autonomous iteration you can actually trust.

Download Zenflow (free) and try the built-in workflows, or build your own loop pattern. The workflows are just markdown, so they're easy to tweak and share.