LangGraph
It's 2:47 AM. Your on-call engineer gets paged. A seven-step multi-agent pipeline that processes high-value insurance claims has failed at step five. The LLM that was supposed to run a compliance verification sub-agent timed out. Now your team is staring at a partially mutated database, an incomplete
Agentic AI
You've survived the proof-of-concept phase. Your multi-agent workflow impressed the stakeholders, the demo ran clean, and now the directive is clear: take it to production, and scale it. That's exactly where the real architectural decision begins, and it's one that will define your team&
AutoGen
Microsoft's AutoGen 0.4 was a landmark architectural shift. It moved away from the conversation-centric model of earlier AutoGen versions and introduced a proper actor-based agent runtime, inspired by the actor model popularized by frameworks like Erlang and Akka. Agents became first-class, message-passing entities. The AgentRuntime became the
Agentic AI
I have enough information to write a comprehensive, expert article. Here it is: If you are a backend engineer right now, you are likely staring at a decision that feels less like a technical choice and more like a geopolitical bet. Do you build your agentic infrastructure on LangGraph? AutoGen?