Our group is looking into developing an AI assistant that goes beyond simple conversations. We want it to actually do things like searching online, handling email communications, and assisting with various workflows. The problem is we don’t know where to begin. There are so many frameworks out there like AutoGen, LangChain, and different API options from OpenAI. The whole ecosystem feels pretty confusing right now. What would be the best approach for beginners? Should we focus on learning one specific tool first, or is there a better way to get started with building autonomous agents?
Went down this exact rabbit hole last year and screwed up everything. Here’s what I wish someone told me: stop obsessing over the ‘perfect’ framework. AutoGen’s great for multi-agent stuff, LangChain’s better for data pipelines. But most production systems I’ve seen? They’re custom builds anyway. Start with one concrete use case. Pick something specific - web scraping, document processing, whatever. Build that with whatever feels right, then expand. Your architecture will figure itself out once you know what you actually need instead of trying to plan everything upfront.
I did this exact same thing six months ago. Skip the complex frameworks at first - build a simple agent with OpenAI’s function calling instead. You’ll learn how AI assistants actually execute tasks, not just chat. Once you get function calls and tool interactions, LangChain becomes way easier to understand. Here’s what clicked for me: all these frameworks just orchestrate the same pattern. AI picks an action, calls a function, gets results, keeps going. Start with a basic email automation project using OpenAI’s API directly, then add complexity later. Trust me, you’ll avoid getting buried in framework docs before you understand what’s actually happening.