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

TL;DR

This paper introduces Agentic Problem Frames (APF), a systematic engineering framework for developing reliable autonomous agents by focusing on structured interactions and formal specifications, validated through industrial case studies.

Contribution

The study proposes APF and AJD as novel engineering tools to enhance the reliability of domain agents, shifting from model-centric to interaction-centric design.

Findings

APF enables systematic control of operational scenarios.

AJD formalizes agent boundaries and contexts.

Case studies demonstrate improved reliability and verification.

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.