The line between an AI agent and an LLM isn't vocabulary, it's who decides the next step. What actually changes in cost, failure modes, testing, and multi-tenant risk once a model starts directing its own loop.
Most comparisons rank CrewAI as the more production-ready of the two. On the one question that decides whether you need a third subscription just to see what your agent did, AutoGen actually comes out ahead.
Every LLM agent framework roundup scores developer experience and integration count. Here is what six of them actually give you for tracing, eval, and multi-tenant isolation, with the API-level specifics the lists leave out.
Prompt engineering optimizes one message. Context engineering decides what a production agent sees before it ever reads that message. Here's where the two actually split, and how to test the difference.
Four multi-agent architecture patterns show up in production again and again. What each one costs in tokens, when a single agent is still the right call, and what breaks first once a second tenant shows up.
Multi-agent LLM systems fail for one of three root reasons, not a random assortment of bugs. The MAST taxonomy, what each looks like in a real trace, and where it shows up in a customer's numbers.