Deep production experience with stateful, policy-controlled agent runtimes and tool-using multi-agent systems for autonomous long-horizon work: durable session lifecycle, context-window management, retrieval and RAG, guardrails, evaluation systems, secure delegation, safe tool use, interruption handling, and recovery—built on established orchestration frameworks or on custom infrastructure of your own design, including MCP and production tool and integration lifecycle management. Advance the models themselves Improve model reasoning and quality through hands-on post-training: reinforcement learning (RLHF/RLAIF), preference optimization, supervised fine-tuning, distillation, and reward and grader design to improve reasoning, tool-use reliability, and domain-specialized behavior across frontier models and smaller, domain-specialized models.