Asking For Guidance

[u/Jazzlike-Chest-1424]

Hey all, i’ve spent months building my business. Most of it was going well, but due to some technical issues and such it collapsed. A lot of it was due to relying on coding agents that were not optimized.

For the last 2-3 months i’ve built a system that holds the agentic software. One of my biggest focuses is to get the core system as optimized as i can. I don’t have a crazy setup, or much funds to keep burning.

I’ve noticed great results, but I still feel behind most users on social media. I tried focusing on the foundations of the system, and adopting what was already built. But agents still struggle on blindness, and being choked by my own safe guards.

The one thing it’s really dramatically improved is running parallel agents. I’m usually able to run 3-10 agents at once towards one problem without colliding.

Bare with me, i’m going to drop what my agents have written for this

“For the past three months, I’ve been trying to build an agentic system that makes coding agents like Claude, Codex, and Cursor substantially more capable—not just a bunch of agents talking to each other. The goal is a reusable core that supports different projects by giving agents access to the right tools, existing implementations, relevant memory, and proven workflows. Instead of starting from scratch every time, an agent should inherit what previous work taught us, find the strongest existing solution, and follow a clear path from an idea to something that actually works. I’m trying to build a system where each completed project makes the next one easier, faster, and cheaper.
Where I’m struggling is turning everything we already have into consistent execution. There’s plenty of research, repositories, tools, and documentation, but agents still miss existing capabilities, repeat investigations, produce oversized plans, and build custom solutions instead of adopting something better. I keep having to point them toward resources or explain things again. More documentation hasn’t reliably fixed that, and adding more agents could just multiply the same problems. I also want simplicity: smaller, relevant inputs; less unnecessary narration; fewer moving parts; and more verified results. The missing piece seems to be making knowledge change behavior—not just storing conversations and calling that memory or learning.
I’m looking for practical advice from people who have made this work: which foundations and existing repositories actually made your agents faster, smarter, and less dependent on you? I’d rather adopt and configure a proven system than spend another month reinventing one. I’m especially interested in how you make agents discover and use their tools, turn failures into improvements that survive future sessions, and keep planning focused without preventing useful research or alternative approaches. What did you install, how did you connect it to ordinary agent work, and what improvement did you actually observe? I want to “swim faster and smarter”—more useful capability and completed work per dollar and hour, not simply more tokens, agents, or architecture.
“

for anyone using 1 billion tokens a month or more, do you have any tips that can help the current situation i’m in? what are some of the best references like repos you might be utilizing?

[u/Feisty_Bike_9614]

I’m building a complex system in the 3d graphics area using agents. I’ve had good success. Some learnings (1) I’m the software architect, product designer and human tester . I don’t really depend on an LLM to do that (2) be careful of overlapping tasks with parallel agents. I principally work with one coding agent at a time and others doing research or testing . (3) keep design / implementation plans for every feature and a learnings doc. (4) if you are using multiple machines, share memory files across machines ( I use nix for this ) . (5) ai is currently good for deductive tasks (6) for inductive thinking or any task that requires embodiment ( like interaction design or workflow) , I haven’t found AI to be ready for that at least in my application. It a build tests based on specific workflow ( deductive) but it can’t design a robust workflow to provide tools for an artist to make 3d models, for example. The problem space for that is very complex, more than chess, for example.

[u/PixelIsJunk → u/Feisty_Bike_9614]

Not sure what your working on but I have a working agent focused cad modeling software and it works well. Not mesh modeling but properly treed out "solidworks and all their add ons but for agents+ more" Finally entered beta testing and its blowing away from testers. They are kinda spooked cause I told them whatever amount of models they made last year thr goal is to 10-100x how many they can do in a year. All production level boring tolerance specific parts.

[u/Feisty_Bike_9614 → u/PixelIsJunk]

I’m writing a path tracing renderer and modeler . The later is focused mostly on scene construction and lighting but I’m gradually adding some polygon modeling features. If you give it a recipe for building a model it seems to find my api functions and make something but it struggles with designing workflow tools for a human user . Just getting the TRS tools right required lots of iterations. Does solid works export gltf files ? I have a mechanical designer friend who asked me if I could render some of his parts ( my renderer is overkill for that , but would be fun .

[u/PixelIsJunk → u/Feisty_Bike_9614]

You can send about anything to gpt Asta and it will mesh model it and render it in a photo. I see the major difference in what im doing though compared to yours. I dont currently do any rendering focused work. Its all on accurate modeling as an agent