As we scale toward agentic, multimodal systems combining LLMs, RLHF, tool-use, and retrieval-augmented generation, what practical architecture best balances reliability, alignment, and cost?

Specifically: should future AI systems converge into a unified agent stack (planner + memory + tools + verifier), or remain modular ensembles of specialized models (reasoner, critic, retriever, executor)? And how should we benchmark “real-world robustness” beyond static evals to reflect continuous learning, distribution shift, and tool failure in production environments?