A Framework for Building Robust AI Agents

Agentic AI has evolved from experimental prototypes to a central paradigm for building intelligent systems. While early demonstrations showcase impressive autonomy through tool use, reasoning loops, and multi-agent coordination, production deployments of agentic systems reveal a different reality, entwined with cost, latency, reliability, and data limitations. Existing approaches, often derived from tutorials or isolated patterns, lack a unifying structure to guide end-to-end system design. At the same time, industry guidance from organizations such as OpenAI, Google, and Anthropic highlights recurring challenges in orchestration, evaluation, observability, and scaling. This gap motivates the need for a more structured, principled approach to agentic AI development. In this paper, we present a framework for designing and building agentic systems that integrates both architectural and data-centric perspectives. The initial formulation of this framework is based on our experience designing and delivering agentic systems. We then performed an extensive literature review across industry guidance and academic research, and grounded the framework in documented practice. The proposed framework is organized around four core building blocks, namely, Business Goals, Pipeline Design, Knowledge Design, and Checks and Balances, which together span the lifecycle from intent definition to production operations. These blocks are articulated across two layers: a conceptual layer that defines what must be addressed, and a component architecture that specifies how systems are realized in practice. Central to the framework is the recognition that agentic systems are not static pipelines, but evolving systems that generate signals, enabling continuous evaluation, feedback, and improvement. This perspective incorporates principles from data science, including evaluation design, feedback loops, and iterative refinement, alongside system-level concerns such as orchestration, scalability, and control. The presented framework aims to provide both a mental model and a practical blueprint for building production-grade agentic systems. By explicitly addressing trade-offs across cost, latency, and reliability, and by embedding observability, evaluation, and feedback into the system design, the framework enables more robust and controllable deployments. Ultimately, the framework highlights that in agentic AI, intelligence emerges not just from models, but from the disciplined integration of components, data, and feedback in real-world environments.