Production-ready agentic AI systems leverage autonomy, reasoning, and adaptive capabilities to solve real-world tasks in dynamic environments. This work presents a framework for designing, validating, and deploying agentic systems, highlighting the construction of typological models, experimental evaluation, and practical impacts in industrial and service domains. We include methodologies, results, and lessons learned to enable robust, scalable, and ethical productionization.
Agentic AI systems have evolved from passive data processors to autonomous agents capable of complex reasoning and decision-making. The drive to productionize these systems is fueled by advances in LLMs, multi-agent architectures, and industry demands for scalable, intent-driven automation. The introduction outlines the pressing need for practical frameworks and evaluation strategies to transform prototypes
Previous studies have defined agentic AI frameworks, classified agentic systems by agency level, and reported experimental deployments in domains such as industrial automation, conversational assistants, and multi-domain robotic control. Recent literature provides benchmarks for comparing agentic vs. non-agentic approaches, measuring reasoning capabilities, and exploring ethical or normative alignment in automated system
Typology Construction: Multi-phase process for defining agentic dimensions—cognitive agency, environmental adaptation, normative alignment, communication protocols.
System Architecture: Root-agent orchestrates sub-agents (task-specific, domain experts) via intent-based interactions and centralized reasoning or decentralized agent pools.
Data Sources: Use of canonical benchmarks (e.g., CMAPSS for industrial automation), synthetic datasets for agent-training, and empirical deployment in operational environments.
Evaluation: Comparative analysis against legacy solutions, qualitative expert interviews, and quantitative metrics (accuracy, efficiency, ROI