Agentic Coding Scam
Earl Brandt of Kira argues the industry's latest pitch — that the real value of AI coding lies in increasingly elaborate agent "harnesses" — doesn't hold up. He walks through a recent EPFL/Apple paper where a bare-bones agent with just a file system and three tools beat sophisticated multi-agent scaffolding across every frontier model tested, with delegation and sub-agent setups actually making results worse. The second half is an interview with a developer, Marcos, who uses Brooks' "No Silver Bullet," Gall's law and Jevons paradox to argue that AI shifts where engineers work rather than eliminating them.
- Researchers at EPFL and Apple tested how much harness an agent actually needs by giving agents 24 hours on the same hardware to fetch data, build a model and test results.
- A minimal agent called Malena — just a file system and three small tools — beat four state-of-the-art harnesses with tree search and parallel workers; on GLM 5.2 it won almost two-thirds of the time while the best elaborate system stayed mostly below 50%.
- Ablation showed the single biggest contributor was giving the model a file system and a shell so it could run its own code and read its own errors; skills libraries, validation rules and search tools made no statistically significant difference, and several additions made things worse.
- Reading the agents' working notes, the complex harnesses treated a whale-identification task as a brute-force search over candidate ideas, while the simple agent built one working pipeline (turning photos into fingerprints) and iterated on it.
- Adding management functions — delegation, supervision, collaboration, plan execution — lowered performance, meaning extra tokens spent on orchestration can create negative value.
- Marcos invokes Fred Brooks' 1986 essay "No Silver Bullet": tools can shrink the friction of coding (syntax, boilerplate) but not the hard part — deciding what the system should do and handling cases like two people booking the same seat or a payment failing halfway.
- Gall's law says complex systems must grow from simple working ones, so prompting "build me an app like Uber" produces something with no structure underneath that can't be patched when it breaks.
- Jevons paradox predicts cheaper software means more software, not fewer engineers; companies cutting teams are throwing away capacity and the institutional knowledge of why systems were built a certain way.
- Brandt's framing: the people pushing the job-apocalypse narrative aren't the ones doing the work, and firms that laid off engineers are already having to buy that expertise back.
This is the paper's core result and the single empirical claim the whole argument rests on.
On the GLM 5.2 model, the bare agent Malena won almost 2/3 of the time. And the best of the elaborate systems got mostly below 50%... the one change that mattered the most was giving the model a file system and a shell.
A clean economic counterargument to the assumption that faster engineers means fewer engineers.
When something becomes cheaper to use, we don't use less of it, we use more of it... Ideas that were never worth the cost before suddenly are, because they've been reduced to a weekend of prototyping.
They're token prediction algorithms with a wrapper of code around them.
A scaffolding or a harness is a bit like training wheels on a bicycle. Sure, they're great to get you started, but ultimately those training wheels end up slowing you down.
AI didn't fire those people, the companies did.