How to Choose the Right Agent Architecture?
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? ...