u/siliconAi

I'm a CS student evaluating whether agent memory infrastructure is a genuine startup opportunity or a feature that OpenAI will ship and kill everyone.

For founders building AI products — is persistent agent memory a problem you'd pay to solve? What would make you trust a third-party memory API with your data?

Not selling anything. Building research around production agent failures specifically around memory. Three things I keep hearing: deduplication noise, context bleeding between projects, and retrieval adding latency on every single turn.

Which of these hurts most? Or is there something else entirely breaking your agents?

Most memory systems today only do ADD. New fact comes in, it gets appended. Old contradicting fact stays. Agent gets confused.

If a memory layer automatically detected contradictions and updated/deleted stale memories — would that fix your biggest pain point? What else would it need to be actually useful in production?

I’m a CS student currently researching how teams building AI agents handle long-term memory and context across sessions.

For those building with LangChain, CrewAI, AutoGen, or custom agent stacks:

• How are you currently storing and retrieving memory?
• Are you using conversation history, vector DBs, Redis, Postgres, or something else?
• What’s the biggest pain point with your current setup?

I’m particularly interested in understanding what breaks when agents move from demos to production.

Not selling anything—just trying to learn from people building real-world systems. Would love to hear about your experiences and architecture decisions.