Academic

Agentic Problem Frames: A Systematic Approach to Engineering Reliable Domain Agents

arXiv:2602.19065v1 Announce Type: new Abstract: Large Language Models (LLMs) are evolving into autonomous agents, yet current "frameless" development--relying on ambiguous natural language without engineering blueprints--leads to critical risks such as scope creep and open-loop failures. To ensure industrial-grade reliability, this study proposes Agentic Problem Frames (APF), a systematic engineering framework that shifts focus from internal model intelligence to the structured interaction between the agent and its environment. The APF establishes a dynamic specification paradigm where intent is concretized at runtime through domain knowledge injection. At its core, the Act-Verify-Refine (AVR) loop functions as a closed-loop control system that transforms execution results into verified knowledge assets, driving system behavior toward asymptotic convergence to mission requirements (R). To operationalize this, this study introduces the Agentic Job Description (AJD), a formal specific

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Chanjin Park (Seoul National University) · · 1 min read · 7 views
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arXiv:2602.19065v1 Announce Type: new Abstract: Large Language Models (LLMs) are evolving into autonomous agents, yet current "frameless" development--relying on ambiguous natural language without engineering blueprints--leads to critical risks such as scope creep and open-loop failures. To ensure industrial-grade reliability, this study proposes Agentic Problem Frames (APF), a systematic engineering framework that shifts focus from internal model intelligence to the structured interaction between the agent and its environment. The APF establishes a dynamic specification paradigm where intent is concretized at runtime through domain knowledge injection. At its core, the Act-Verify-Refine (AVR) loop functions as a closed-loop control system that transforms execution results into verified knowledge assets, driving system behavior toward asymptotic convergence to mission requirements (R). To operationalize this, this study introduces the Agentic Job Description (AJD), a formal specification tool that defines jurisdictional boundaries, operational contexts, and epistemic evaluation criteria. The efficacy of this framework is validated through two contrasting case studies: a delegated proxy model for business travel and an autonomous supervisor model for industrial equipment management. By applying AJD-based specification and APF modeling to these scenarios, the analysis demonstrates how operational scenarios are systematically controlled within defined boundaries. These cases provide a conceptual proof that agent reliability stems not from a model's internal reasoning alone, but from the rigorous engineering structures that anchor stochastic AI within deterministic business processes, thereby enabling the development of verifiable and dependable domain agents.

Executive Summary

This study proposes Agentic Problem Frames (APF), a systematic engineering framework for developing reliable domain agents. The framework focuses on the structured interaction between the agent and its environment, establishing a dynamic specification paradigm through the Act-Verify-Refine (AVR) loop. The Agentic Job Description (AJD) formal specification tool is introduced to define jurisdictional boundaries and operational contexts. The efficacy of APF is validated through two case studies demonstrating the systematic control of operational scenarios within defined boundaries. The study's primary contribution is the recognition that agent reliability stems from rigorous engineering structures rather than internal model reasoning alone.

Key Points

  • ▸ Agentic Problem Frames (APF) is a systematic engineering framework for developing reliable domain agents.
  • ▸ APF establishes a dynamic specification paradigm through the Act-Verify-Refine (AVR) loop.
  • ▸ The Agentic Job Description (AJD) formal specification tool is introduced to define jurisdictional boundaries and operational contexts.

Merits

Strength

APF provides a structured approach to agent development, ensuring reliability and dependability.

Systematic Engineering

The framework shifts focus from internal model intelligence to the interaction between the agent and its environment.

Demerits

Limitation

The study relies on two case studies, which may not be representative of all possible scenarios.

Complexity

The APF framework may add complexity to the development process, particularly for unexperienced developers.

Expert Commentary

The study's primary contribution is the recognition that agent reliability stems from rigorous engineering structures rather than internal model reasoning alone. This is a crucial shift in perspective, as it acknowledges the importance of designing systems that can operate within well-defined boundaries and meet specific requirements. The introduction of the Agentic Job Description (AJD) formal specification tool is also noteworthy, as it provides a standardized approach to defining jurisdictional boundaries and operational contexts. However, the study's reliance on two case studies may limit its generalizability, and the added complexity of the APF framework may pose challenges for developers. Nevertheless, this study provides a valuable contribution to the field of artificial intelligence and autonomous systems, and its findings have significant implications for both practical and policy considerations.

Recommendations

  • ✓ Recommendation 1: Future studies should explore the application of the APF framework in diverse domains and scenarios to further validate its efficacy.
  • ✓ Recommendation 2: The development of standardized tools and methodologies for designing reliable domain agents should be prioritized to ensure widespread adoption and implementation.

Sources

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