IndieDevDan has a pattern he calls the Ralph Wiggum. The idea: before an AI agent takes any action, it first states what it observes. Named after the Simpsons character who narrates the obvious — "I'm learnding!" — the pattern forces an agent to describe what it sees before it does anything about it.
That's it. That's the whole pattern.
It sounds trivial. It isn't. The gap between "agent that reads the code and immediately starts editing" and "agent that describes what it's looking at, then edits" is the gap between an agent you babysit and one you can actually trust with a task.
What the pattern actually does
Here's the problem it solves. You point an AI agent at a codebase and say "refactor this component." Without any observation step, the agent reads the file, forms an internal representation you can't see, and starts making changes based on that invisible understanding. If it misread the code — wrong framework, wrong version, wrong intent — you won't know until the damage is done.
The Ralph Wiggum pattern inserts a checkpoint. The agent has to say out loud: "I see a React component using class-based syntax. It has three lifecycle methods. componentWillReceiveProps is deprecated. There's no cleanup in componentDidMount." Now you can catch the misunderstanding before any code gets written. Maybe it's not actually React. Maybe componentWillReceiveProps is there intentionally for a legacy compatibility layer. You see the agent's model of the situation and can correct it.
A simple before and after:
# Without the pattern — agent acts on invisible assumptions def refactor(file_path): new_code = agent.generate_refactored_code(file_path) write_file(file_path, new_code) # hope it understood correctly # With the pattern — agent shows its work first def refactor(file_path): observations = agent.describe(file_path) print(observations) # you see what the agent thinks it's looking at if observations_look_right(observations): new_code = agent.refactor_based_on(file_path, observations) write_file(file_path, new_code)
Ten lines. That's all the code this concept needs.
The principle underneath
The Ralph Wiggum pattern is a specific instance of something broader: verification before action. It shows up everywhere once you start looking.
Code review works because someone reads the diff and describes what they think it does before approving it. Rubber duck debugging works because explaining the problem out loud forces you to articulate your understanding — and often reveals where that understanding breaks. Pair programming works in part because the navigator is constantly verbalizing observations about what the driver is doing.
The insight is that articulating understanding is itself a verification step. When you force an agent to describe what it observes, you're not just creating a log. You're forcing the model to commit to a specific interpretation of the situation. That commitment makes misunderstandings visible. Silent misunderstandings are the ones that cause real damage.
I already use this — I just didn't have a name for it
I haven't deployed long-running autonomous agents for 24-hour stretches. I should be honest about that. But the principle underneath the Ralph Wiggum pattern — verify understanding before taking action — shows up constantly in how I work with Claude Code.
Plan mode is basically Ralph Wiggum. When I kick Claude Code into plan mode, it reads the codebase and describes what it sees before proposing any changes. "Here's the file structure. Here are the dependencies. Here's what I think this component does. Here's what I'd change and why." I read that description, catch any misunderstandings, and only then let it execute. Same pattern, different packaging.
CLAUDE.md files work this way too. When I write a CLAUDE.md for a project, I'm front-loading context so the agent's observations start from a more accurate place. The agent reads the CLAUDE.md, forms its understanding, and when it describes what it sees, that description is better because it had good context going in. The observation step is only as good as the context feeding it.
Even something as simple as asking Claude "what do you see in this file before you change anything?" is the Ralph Wiggum pattern in its most basic form. I've been doing that instinctively for months. IndieDevDan just gave it a name — and naming things matters, because named patterns are patterns you can reason about, teach, and deliberately apply.
The thread-based engineering context
IndieDevDan places the Ralph Wiggum pattern inside a broader framework he calls thread-based engineering. A thread is a unit of agent work: you prompt, the agent works, you review. The taxonomy goes further — L-threads for long-running autonomous work, P-threads for parallel agents, C-threads for chained multi-phase tasks.
The Ralph Wiggum pattern is what makes L-threads possible. If an agent is going to work autonomously for an extended period, it needs some mechanism to self-check. Observation loops provide that mechanism. The agent periodically describes what it sees, compares that to what it expected, and self-corrects when there's a mismatch. Without that, long-running agents drift. With it, they have a way to stay on track — or at least to fail in ways you can diagnose after the fact.
I find the framework useful as a vocabulary for thinking about agent architectures, even though my own work hasn't pushed into multi-hour autonomous runs yet. The categories clarify what's actually different between "I ran a quick prompt" and "I set up a pipeline that runs overnight." They're different engineering problems, and having names for them makes the differences easier to reason about.
Where the pattern has limits
Observation loops add latency. Every time an agent stops to describe what it sees, that's tokens spent on description instead of action. For quick, well-understood tasks — formatting a file, running a known migration — the observation step is overhead you don't need. The pattern is most valuable when the task involves ambiguity, when the agent might misunderstand the situation, when the cost of acting on wrong assumptions is high.
There's also a subtler issue. An agent can describe what it sees and still be wrong in ways the description doesn't reveal. "I see a React component with three props" — fine, but does the agent understand that one of those props is a render prop that controls layout behavior? The observation might be technically accurate but miss the important context. Observation is necessary but not sufficient. It catches surface-level misunderstandings. Deeper conceptual errors can survive the observation step intact.
This isn't a reason to skip the pattern. It's a reason to not treat it as a silver bullet. It reduces a category of errors — the ones caused by agents acting on incorrect basic understanding. It doesn't eliminate errors caused by agents that understand the surface but miss the deeper structure.
What this connects to
The broader principle here — that AI agents become more reliable when you make their reasoning visible — is one of the few things I'm genuinely confident about in this space. Most of the AI discourse is hype layered on hype. But "make the agent show its work" is just good engineering practice applied to a new substrate.
It connects to something I keep coming back to: the bottleneck in AI agent reliability isn't the model's capability. It's the information architecture around the model. How you structure context, how you make the agent's understanding inspectable, how you build checkpoints where misunderstandings can surface. The Ralph Wiggum pattern is one tool in that kit. Plan mode is another. CLAUDE.md files are another. They're all variations on the same underlying move — make the agent's internal model visible so you can verify it.
IndieDevDan deserves credit for naming this clearly and placing it in a coherent framework. The thread-based engineering discussion is worth watching if you're thinking about agent architectures beyond simple prompt-response loops. The pattern itself is simple enough to start using immediately. The question that interests me more is what other observation-style patterns exist that we haven't named yet — and what it looks like when these become standard practice rather than techniques individual engineers discover on their own.