Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering

2026-04-09

AI Summary

This work addresses the limitations of large language model agents in reliably executing complex tasks due to inherent cognitive burdens that cannot be overcome by parametric capabilities alone. The authors propose a system-level framework centered on externalization, treating memory, skills, and protocols as three interdependent cognitive artifacts coordinated through a unified execution architecture. Grounded in cognitive artifact theory, the framework integrates modular external components with runtime coordination mechanisms, offering a cohesive analytical lens for agent infrastructure. The study demonstrates that performance gains increasingly stem from external cognitive infrastructure rather than model scaling alone, highlighting promising directions such as self-evolving architectures and shared infrastructures, while also identifying associated challenges in evaluation and governance.

Abstract

Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the model to recover internally are now externalized into memory stores, reusable skills, interaction protocols, and the surrounding harness that makes these modules reliable in practice. This paper reviews that shift through the lens of externalization. Drawing on the idea of cognitive artifacts, we argue that agent infrastructure matters not merely because it adds auxiliary components, but because it transforms hard cognitive burdens into forms that the model can solve more reliably. Under this view, memory externalizes state across time, skills externalize procedural expertise, protocols externalize interaction structure, and harness engineering serves as the unification layer that coordinates them into governed execution. We trace a historical progression from weights to context to harness, analyze memory, skills, and protocols as three distinct but coupled forms of externalization, and examine how they interact inside a larger agent system. We further discuss the trade-off between parametric and externalized capability, identify emerging directions such as self-evolving harnesses and shared agent infrastructure, and discuss open challenges in evaluation, governance, and the long-term co-evolution of models and external infrastructure. The result is a systems-level framework for explaining why practical agent progress increasingly depends not only on stronger models, but on better external cognitive infrastructure.

Problem

• externalization
• LLM agents
• cognitive infrastructure
• memory
• harness engineering

Innovation

• externalization
• LLM agents
• cognitive artifacts
• harness engineering
• agent infrastructure