Structured Cognitive Loop for Behavioral Intelligence in Large Language Model Agents (Extended Revision: From Behavioral Architecture to Epistemic Accountability)
arXiv:2510.05107v5 Announce Type: replace Abstract: The central challenge for AI agents is not only performance but accountability. Agents that act through opaque prompt sequences may produce correct outputs, but they provide little basis for verifying why an action was permitted, where an error occurred, or how responsibility should be assigned. This paper presents the Structured Cognitive Loop as an architecture for accountable behavior in large language model agents. SCL separates cognition, memory, control, and action into distinct modules. The language model proposes. External memory pres
The rapid deployment of LLM agents highlights the urgent need for verifiable and accountable AI systems, moving beyond opaque black-box operations towards robust architectures.
For strategic readers, this work addresses a critical vulnerability in AI deployment by proposing a framework for accountability, essential for regulatory compliance, trust, and managing complex AI interactions in sensitive domains.
The focus expands from mere performance to explicit accountability and verifiability in AI agents, enabling better error tracing, responsibility assignment, and system governance.
- · AI developers
- · Enterprises deploying AI
- · Regulatory bodies
- · AI auditing firms
- · Developers of opaque AI systems
- · Disruptive AI startups prioritizing speed over accountability
The Structured Cognitive Loop could become a standard architectural pattern for explainable and accountable AI, especially in high-stakes applications.
Increased trust and transparency in AI agents might accelerate their adoption in critical infrastructure, healthcare, and financial services.
Formal accountability metrics and audit trails in AI could lead to new legal frameworks and insurance markets specifically tailored for AI system liabilities.
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Read at arXiv cs.AI