Beyond the Black Box: A Roadmap to Reliable Agentic Systems¶
We are hitting the ceiling of what pure probabilistic models (LLMs) can achieve in production. When building Agentic RAG systems, the reliance on next-token prediction often leads to stochastic failures, hallucinations, and weak long-horizon planning.
This video outlines a holistic approach to reliability by evolving from purely neural to neuro-symbolic architectures. It lays out a strict dependency chain—starting with knowledge graphs for context, moving to PDDL solvers for causal logic, and finishing with GRPO/Reinforcement Learning for optimization. The result is a blueprint for agents that are safe by design and fast by training.