This sounds like another version of "As a chat becomes longer, the guardrails seem to become fuzzy". You can't use all of the context window bc at the end, the output would not respect the constraints (or guardrails) but to reliably produce production grade code you want the model to have expansive awareness which fills up the context window pretty quickly. It's like saying "Keep everything in mind from these 6 directories - and make this <insert ticket> change" - but keeping everything in mind already fills it's context window which makes it lose it's ability to follow the constraints (or guardrails).

“Our systematic study exposes a phenomenon of constraint decay in LLM-based coding agents. While current models excel at unconstrained generation, their performance drops when forced to navigate explicit architectural rules. For end-users, this dichotomy implies that agents are reliable for rapid prototyping but remain unreliable for production-grade backend development.”

One major weakness of this study is that they didn’t fully test frontier models for cost reasons, so the specific performance results should be taken with a grain of salt. But the overall conclusion that models degrade when both behavior and architecture must be correct is interesting, and something to keep an eye on.

> Our findings reveal a phenomenon of constraint decay: as structural requirements accumulate, agent performance exhibits a substantial decline.

I have exactly the inverse findings on my end. The bigger and more legacy the codebase, the more accurate the patches become.

The harness itself seems to be the most important part. I use a recursive loop that primes the root context based on the user prompt each time. My agent will often make over 100 tool calls to sql and git before it finally decides to apply a patch. If I was greenfield, there would be nothing to query or constrain against.

Reminds me of the recent paper about delegating document editing tasks to LLMs across different disciplines [1]. That paper found that programming was the only discipline most LLMs can perform long horizon tasks on without accumulating errors & corrupting the document.

I've only read the abstract of this one so far but it seems like this paper has zoomed in on programming with greater fidelity and shown a similar phenomenon. But not about long horizon tasks, more like "long style horizons" of larger sets of structural constraints.

[1] https://arxiv.org/abs/2604.15597

Discussion: https://news.ycombinator.com/item?id=48073246

Also they used languages with dynamic typing like Python & JS. In my experience a statically typed codebase is easier to maintain for humans so maybe it is also for agents.

When using Codex/Claude Code with Go code I cannot count the times the agent does some change, runs a build to check for errors, find some and fix them.

This is interesting, anecdotally I have felt like I was having better luck with raw sqlite than using an ORM in a recent typescript project, using raw sqlite queries vs drizzle

Odd they used GPT-5.2 and not GPT-5.2-codex. i.e. the one optimized for coding agent tasks.

   tasks spanning eight web frameworks

Does anyone else have this experience that LLM create better pure html+CSS+js than work with existing frameworks?

These things don’t think. We’re going to have to reiterate this for a long time, I fear.

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