Applying a naive 'Ralph Wiggum loop' (repeatedly re-running the same instruction) on tools not designed for it fails badly at scale, as shown by an autonomously-built C compiler project where a basic hello-world program didn't even compile.
Sidhant cites an autonomously-built C compiler project—where hello world didn't even compile—as evidence that simply looping an agent on a spec ('Ralph Wiggum loop') fails without real context and agent engineering. ✦ AI generated
Sidhant Pardeshi · The TWIML AI Podcast · 2026-03-10 · original ↗
starts at this moment · 11:27
“If the human is in the loop, how autonomous is the development? How do you think about that distinction and nuance?”
you had uh for example, Anthropic put out this huge project that was a C compiler. Um and the very first issue, not very first, but one of the most popular issue on that forum is that this repo should not exist because a hello world does not compile on this compiler, right?
verbatim transcript · starts at 11:27
11:27issue, not very first, but one of the most popular issue on that forum is that this repo should not exist because a hello world does not compile on this compiler, right? >> [laughter] >> So, you have problems like that when you try to apply what is called the Ralph Wiggum loop to you know existing uh let's say tools that were not designed for that like Ralph Wiggum loop is
11:52essentially just running the same thing again and again till it gets to you know the correct answer. The point I I I'm trying to make is it's not just about sending giving AI a spec. It's all about context engineering and agent engineering. So, um context engineering is about you know giving the AI the right amount of context in at the right time. The problem what happens is at scale across
12:20the enterprise when you have like hundreds and thousands of developers not everyone is using the tool with the same level of efficacy. So, all of the CI tools um code x thought code you name it require a significant amount of setup like you have you connected to the right MC PI using the right skills. Are you using the right prompts? The same prompts that work for Anthropic don't
12:43work for open AI. Like for example, open AI doesn't use XML tokens in their training but Anthropic does, right? So, if you use XML tokens with with open AI or if you shout in your prompts which you have to do with Claude a times you have to shout at Claude to get it to listen to you. Uh it's it's ineffective strategy and then but but it's widely held that
13:04GPT 5.3 gets many things right that Opus does not, right? So, there's all of these complex agentic engineering. So, that's the part that is agentic engineering where you recruit the right agent with the right set of prompts and tools with the right level of prompt engineering for the right task, right? Because there are definitely tasks that GPT is better than Opus for. And then there's context engineering which
13:27optimizes for giving the agent the right amount of information at the right time and for getting it focus on the the smallest possible task that is efficient for that agent, right? Without like overdoing it or underdoing it. So, when you apply those two at scale, and you solve, you know, some of the most important challenges, like for example, context limits. So, we've have we have a very creative solution to only at least