The more I work on AI agents, the more I feel like the actual problem isn’t the LLM.
It’s the infrastructure mess around it.
Every serious agent stack today eventually turns into some version of this:
LLM + vector DB + cache + retrieval pipeline + connectors + permissions + memory layer + observability + audit logs + orchestration glue
And then the team spends months trying to answer questions like:
- What exactly does the agent know right now?
- Why did it retrieve this?
- Is the memory fresh?
- Can this be audited?
- Why is latency suddenly terrible?
- How do we deploy this inside enterprise environments?
At some point, it starts feeling like teams are not building agents anymore.
They’re building distributed context engineering systems.
What’s interesting is that a lot of the current stack seems inherited from search/retrieval architecture, not something fundamentally designed for long-running autonomous agents.
Feels like there’s a missing abstraction somewhere:
a proper system for agent memory, context, permissions, and actions to live together instead of being stitched across multiple tools.
We’ve been exploring this idea at Areev AI and built an early version of what we’re calling an “agent harness database” around this concept. Still early, but increasingly feels like the current stack won’t scale cleanly for production-grade agents.
Curious if others building agentic systems are running into the same thing:
- What’s the messiest part of your stack today?
- Where do things usually break?
- What do you think the missing infrastructure layer is?