Integrating LLM agent components into a unified end-to-end framework

Determine how to integrate key components of large language model (LLM) agents—including planning, reflection, tool use, and life-long learning—into a single unified architecture and optimize the resulting agent end-to-end.

Background

The paper notes that prior work has identified several essential components for LLM agents, such as planning, reflection, tool use, and life-long learning, but lacks a unified framework that brings these together under a single, end-to-end optimization process.

This uncertainty motivates the AGILE framework proposed in the paper, which unifies modules (LLM, memory, tools, executor) and formulates agent construction as reinforcement learning. The open question reflects the broader challenge in the field beyond the specific solution presented.

References

However, it remains unclear how to integrate all components into a unified framework and optimize them end-to-end.

— AGILE: A Novel Reinforcement Learning Framework of LLM Agents  (2405.14751 - Feng et al., 2024) in Section 1, Introduction (page 1)

In particular, integrating multimodal perception, reliable tool orchestration, and continual performance self-evolution within a unified remote sensing agent architecture remains an open challenge.

— RingMoClaw: An Experience-Inspired Multi-Agent Framework for Self-Evolving Research in Remote Sensing  (2609.00814 - Kang et al., 1 Sep 2026) in Section 2.3, OpenClaw-Based General Agent Frameworks