There is no real “memory” unless YOU manage it.

State Management & Context Drift in Multi-Step AI Workflows This is one of the biggest hidden problems in AI automation systems. Everything works… until it doesn’t. Here’s what’s actually happening under the hood 👇 LLMs are stateless by design Every request is independent. There is no real “memory” unless YOU manage it. So in multi-step workflows: Step 1 generates output Step 2 depends on it Step 3 modifies it If state is not structured → context breaks. Context drift starts silently Over multiple steps: Important details get dropped Irrelevant tokens get added Meaning starts to shift Result: 👉 Output becomes inconsistent 👉 Agents behave unpredictably Token limits make it worse You can’t pass full history forever. So you: Trim context ❌ Summarize ❌ Both introduce: Information loss Hallucination risk No single source of truth In most AI frameworks: Some data in prompts Some in memory Some in DB This fragmentation causes: Conflicts Outdated context Logic errors Multi-agent = expon