Ask HN: How are you keeping AI coding agents from burning money?

In what settings do you mean - there are multiple strategies, I think building your own compaction layer in front seems a bit over-kill ? have you considered implementing some cache strategy, otherwise summary pipelines - I made once an agent which based on the messages routed things to a smaller model for compaction / summaries to bring down the context, for the main agent.

But also ensuring you start new fresh context threads, instead of banging through a single one untill your whole feature is done .. working in small atomic incrementals works pretty good

I had gotten a student/ultra code for antigravity promo for three months, so I was using that, but that finally ran out this month. Currently Im using windstream and flipping between claude as my left brain and code extraction and the higher context but cheaperish models there.

honestly though, im getting to a point where im running custom project mds that flip between different models for different things, using list outputs depending on what it finds and runs. (I have two monorepo projects, and one thats a polyglot microengine that jumps using gRPC communication.)

The mds are highly specialized for each project as each project deals with vastly different issues. Cycling through the different pro accounts and keeping the mds in place over it all is helping me not kill my wallet.

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AI outputs often feel like a gacha game. Paradoxically, the 'expensive' tokens are sometimes the cheapest in the long run. In my experience, higher-end models have a much higher 'one-shot' success rate. You aren't just saving on total token count by avoiding loops; you’re saving engineering time, which is always the most expensive resource anyway.

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By not using it. The tech is flawed. It hallucinates. It's not production ready. I've said it before, and I will say it again. Anyone using AI in a production environment is a fucking idiot.

Don't use tech with deep, unresolved flaws and you won't get fucked.

Would you find it acceptable if Postgresql occassionally hallucinated and returned gibberish? Fuck no.

Wny is this okay with ANY software? Answer, it's not. AI IS NOT READY.

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Kinda of an adjacent question but do you think the token/usage way of paying for things will stick? I still think people would rather pay a monthly subscription for a seat.

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cost control is a policy problem - we certainly don't need to use opus 4.6 for a simple test refactor, but many people (including myself) default to it anyways. we need a way to measure cost / performance for agents on individual repos, with individual types of tasks, to get a better sense of what tasks can be trusted to cheaper agents, and what tasks must be routed to the SOTA

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Mine have burned a lot of money! Right now, I'm trying to keep the context smaller. It takes a lot of discipline, though, to have a system that gives enough context to do the work but not so much the agent can go off doing new/crazy stuff.

Some things that I know how to do, I just run myself. If starting the tests is a bash command, I asked the AI to create bash script that does this, and then I run it myself. Same, with the build, deploy and other similar tasks. For some no so important tasks, I use different model, like GLM, which is cheaper. Then I save the result of the, let's say bug analysis, or code review, and ask my main model (Opus) to read the document and execute the task. This way I use my expensive model to write the tasks, but the cheaper one to do the analysis.

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Interesting how 50% of the comments in this thread, flagged or not, are from green accounts.

Eternal September 2.0: the LLM edition.

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Not really sure if you mean setups like OpenClaw as well, but I ran into pretty similar issues there.

That was actually a big part of why I started building my own agent system, Arc agent, mostly just to see if I could solve some of the problems every agent faces. A few practical things helped, putting limits on iteration/retry loops so the agent can’t wander forever, being stricter about context/tool handling once external skills are in play and adding a simple cost/token counter so I could at least see where usage was going. I also tried to reduce the usage of llm whereever a simple code would work more reliably.

I tried to tackle few other problems like downtime during context compaction etc, but yeah the project is still work in progress and i am experimenting with diff stuff.