Multi-Agent

Context management for long-horizon tasks and coordination across multiple execution units are among the central problems in current Agent research. In LLM Agents I introduced the three core modules of an Agent—planning, memory, and tool use; in Self-Evolving Agents and the FlashInfer Contest Summary I discussed the paradigm of Harness Engineering: humans design the constraints, feedback, and evaluation, while the Agent iterates inside a controlled closed loop to produce verifiable results. This post focuses on a more concrete layer of the problem: when tasks grow longer and more complex, what architecture should we use to organize an Agent? ...

From the evolution of the GPT series, researchers have gradually realized that as long as model parameters, training data, and compute resources are continuously scaled up, the performance of large models improves along a stable and predictable path. This predictability is characterized by Scaling Laws, which provide the theoretical foundation and practical confidence for high-cost pre-training. As model scale, alignment techniques, and inference-time compute co-evolve, the boundaries of AI capabilities are being systematically pushed. Scaling laws are not only the foundation for building next-generation models but also a key methodology for continuously improving model capabilities under compute constraints. ...