LangChain, LangGraph, CrewAI, PydanticAI — no shortage of AI orchestration frameworks. I evaluated all of them and built my own. Not NIH syndrome. When you need failure-mode-driven mutation strategies, phase-aware multi-model routing with different temperatures, and adaptive evolution based on trajectory analysis, the abstraction layers of general-purpose frameworks become obstacles to route around.

Letting AI drive research workflows doesn’t mean letting AI decide how the system runs. I made a key separation: AI is just the operator, the execution engine is the law. This decision came from a failure.

Most systems are designed to run first, then audited as an afterthought. I inverted the order — first define what questions the system must answer when things go wrong, then work backwards to what each layer must record. This inversion reshaped the entire architecture.

MCP’s JSON-RPC transport works fine. The real problem: natural language rules have no code-level enforcement — the LLM can completely ignore your instructions. I designed the Intent Validator pattern to close this gap.

Multi-agent is the hot paradigm in AI engineering. I chose single agent + state machine for my AI-driven quant research system. Not because multi-agent is too hard, but because the problem structure doesn’t match.