What is Your Stack for Building AI Agents?
Let's talk about real AI agents, not AI chatbots. What technologies do you use to create autonomous systems like this?
Shared our approach here and excited to hear about yours.
Let's talk about real AI agents, not AI chatbots. What technologies do you use to create autonomous systems like this?
Shared our approach here and excited to hear about yours.
Seedium, focusing on the architectural distinction between "chatbots" and "autonomous systems" is the right move for 2026. By prioritizing agentic loops and decision-making logic over simple prompt-response pairs, you're highlighting the exact shift toward high-utility infrastructure that founders actually need to scale operations.
I’m currently running Tokyo Lore, a project that highlights high-utility logic and validation-focused tools—specifically the kind of autonomous systems you’re discussing. Since you’re building the definitive stack for agentic AI, entering your project could be the perfect way to turn your technical approach into a winning case study while your odds are at their absolute peak.
Mine's deliberately boring: a ~$9/mo VPS as the always-on orchestrator, a consumer laptop with a small GPU for local inference on anything sensitive, and a gateway proxy (LiteLLM) in front of every paid API so each agent holds a budgeted virtual key instead of a real one — a misbehaving agent fails closed instead of running up a bill. The piece I'd never skip again: a second model whose only job is adversarial review of the first one's output, capped at 3 rounds. It catches overclaims and quiet errors the drafting model never sees in its own work. Agents grading their own homework is a mirage.
This is a timely post. I’ve spent the last few months architecting an AI layer for a Next.js 16 CMS, and the 'stack' decision was the hardest part.
We eventually landed on: Next.js 16 (RSC) + Vercel AI SDK + OpenRouter (BYOK architecture). >
The biggest 'aha!' moment for us was moving away from raw text outputs. We now use constrained decoding to force the models to output strict JSONB blocks that insert directly into our PostgreSQL database. It killed the 'sanitization lag' and preserved our 100/100 Lighthouse scores because there’s zero intermediate parsing.
Are you seeing a lot of latency when parsing agent outputs into your frontend, or are you doing the 'cleaning' on the server side before it hits the DB?