Calling an LLM API is easy, but making an AI agent survive in production is a nightmare. Here is how Logic aims to solve the eval, RAG, and routing hell.
Are you guys sick of AI demos that look like pure magic on localhost but turn into absolute brain-dead potatoes in production? Yeah, making an LLM API call takes like 3 lines of code, but actually making it do useful shit in the real world is a complete dumpster fire.
Scrolling through Product Hunt today, a tool called Logic caught my eye (bagging around 250 upvotes). Steve, the co-founder, hit the nail on the head: when building AI agents, the LLM call is the easy part. The hard parts that will drain your soul are evals, RAG, observability, fallback mechanisms, model selection, and managing latency.
Logic's fix? You write a plain-English, structured "spec" defining what the agent should do. Boom. You get back a fully managed agent ready to be called via REST, MCP, a Web UI, or even email. It handles 130+ document formats, does semantic searches, and calls HTTP APIs. The craziest part is the Smart Model Routing—it juggles OpenAI, Anthropic, Google, and open-source models under the hood to dodge downtime and save your wallet.
Long story short, the era of writing raw prompts and manually deploying spaghetti code to a random cloud vps is dying. Turning plain specs into production-ready agents is the new meta.
The survival lesson here? Don't build your own evals, observability, and routing infrastructure from scratch unless you're masochistic or swimming in VC money. Leverage proper ai tools to handle the boilerplate. Modern dev work isn't just about typing fast; it's about system architecture and keeping the whole thing from crashing and burning.
Source: Product Hunt - Logic
