The gap between a Helpful Assistant and a Senior Engineer

January 4, 2026

Let’s suppose you asked an AI coding agent to “implement a CLI calculator”. Imagine if, instead of only writing short Python script, it also started building an automated test suite, a crash reporting mechanism and a telemetry subsystem. You’d be like, “What the fuck is this?”

But now let’s say that you were planning to release this project to users. It would be clearly negligent to not have an automated test suite. A crash reporting mechanism might be overkill for a simple calculator, but for more complicated CLIs interacting with the real world, it may not always be feasible to have reproducer, in which case crash logs are essential. Similarly, a telemetry subsystem would be wildly inappropriate for an open source local-only calculator, but it could make sense for a networked application or a corporate tool of all consenting users. One of the important functions of a senior engineer is to be able to evaluate the context a software project lives in and figure out if we need to do something, even if it isn’t explicitly asked for. This is contrast to a helpful assistant, who is first and foremost obligated to follow the user’s instructions. This leads to a gap between a Helpful Assistant and a Senior Engineer.

In principle, you could prompt the LLM agent to act like a Senior Engineer. In fact, why stop at Senior, let’s tell the LLM to be a Staff Engineer! Imagine that scaling continues: what would you expect the LLM to do when instructed to act in this way? Well, imagine a human L7 engineer who has just been hired by a big tech company to head up some big, new, multi-year initiative. Will they say, “Sure, I can help with that!” and start busily coding away? Of course not: they will go out and start reviewing code, reading docs, talking to people, asking questions, shadowing oncalls, doing small starter tasks–they will start by going out and building context. Here, the “helpful assistant” frame for LLMs is limiting: sure, Claude might ask you a few questions to clarify the task upfront, but if your coding agent starts asking you about “relevant partner teams” and “org-wide priorities for this half” you are definitely going to raise an eyebrow.

What would take for an LLM to be able to act like a Senior Engineer?

Perhaps prompting is all you need, and you just need to write enough information about the surrounding context for a project, and once you feed in enough tokens, a smart model can infer the rest of the details you didn’t explicitly right down. This context would be bespoke for every project; you would have to redo this exercise every time you had a new project!

Perhaps you can instead prompt a model on how to operate agentically to get the context it needs. This prompt here might be more reusable. But the model may need to actually do wetwork (e.g., talk to humans) to get all of the information it needs. And remember the old saying: the more generic the advice is, the less useful it is. Specificity is king, which leads to…

Let’s say we solve continual learning. Instead of crafting the perfect prompt upfront; you could just drop the model as an “embodied” software developer. It reads code, talks to people, does projects, and in doing so slowly develops its latent context, in the same way a human engineer does. Building context will often be bottlenecked in the same way humans are: you can’t get experience related to pushing a feature to production, until you’ve actually pushed the feature to production (however long that takes).

But just like how you shouldn’t micromanage a Senior Engineer, all of these approaches involve fundamentally different expectations about what an AI coding agent should do, and so even if a model and scaffold are capable of doing these things, it is altogether another question if it will be asked to behave in this way. So let’s not take it as a foregone conclusion that METR task times will keep following the empirical trendline: I expect a phase transition when the context an LLM needs to do a good job exceeds the capability of scaffolding to provide on the fly.