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[2602.19065] Agentic Problem Frames: A Systematic Approach to Engineering Reliable Domain Agents
Summary
The paper introduces Agentic Problem Frames (APF), a framework for developing reliable domain agents by focusing on structured interactions and dynamic specifications, validated through case studies in business travel and industrial equipment management.
Why It Matters
As AI systems evolve, ensuring their reliability becomes critical. This research addresses the risks associated with current development practices by proposing a systematic approach that enhances agent reliability through structured engineering, which is essential for industrial applications.
Key Takeaways
- Agentic Problem Frames (APF) provide a structured approach to developing reliable AI agents.
- The Act-Verify-Refine (AVR) loop enhances system behavior by integrating runtime specifications.
- The Agentic Job Description (AJD) formalizes operational contexts and evaluation criteria.
- Case studies demonstrate the practical application and effectiveness of the APF framework.
- Reliability in AI agents is achieved through rigorous engineering rather than solely internal model reasoning.
Computer Science > Artificial Intelligence arXiv:2602.19065 (cs) [Submitted on 22 Feb 2026] Title:Agentic Problem Frames: A Systematic Approach to Engineering Reliable Domain Agents Authors:Chanjin Park (Seoul National University) View a PDF of the paper titled Agentic Problem Frames: A Systematic Approach to Engineering Reliable Domain Agents, by Chanjin Park (Seoul National University) View PDF HTML (experimental) 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 efficac...
