The AI landscape moves at a breakneck speed. Every few weeks, a new frontier model or an ultra-efficient open-weights architecture drops—whether it's the latest reasoning breakthroughs from Moonshot AI’s Kimi series or open-source LLMs pushing context windows into millions of tokens
Models are getting faster, cheaper, and objectively smarter at reasoning
However, as we push these models into building truly autonomous AI agents, we hit a familiar, frustrating wall: LLMs are fundamentally non-deterministic state machines
While a smarter model reduces reasoning errors, it doesn't solve system architecture failures. If you've built agents using standard frameworks, you've likely ran into these walls:
State Drift: Agents lose track of their execution context over multi-step tasks
