AgenticLens: An Intelligent Optimization Framework for Enterprise Agentic AI Systems Using Semantic Workflow Graphs, Federated Model Orchestration, and AI Economics

Large language models have accelerated the adoption of agentic artificial intelligence across enterprise software, enabling autonomous planning, reasoning, and execution at a scale that earlier automation could not achieve. Most current research emphasizes model capability and reasoning performance, while enterprise deployments confront a different set of constraints tied to inference cost, token efficiency, governance, latency, and operational reliability. These constraints intensify in mission-critical environments such as financial services, where deployed systems must satisfy demanding requirements for resilience, auditability, regulatory compliance, and predictable operational behavior. This paper introduces AgenticLens, an enterprise optimization framework for designing, analyzing, and continuously improving agentic AI systems. It treats enterprise AI as a coordinated ecosystem of interacting agents, retrieval components, reasoning workflows, and model execution paths. AgenticLens combines Semantic Workflow Graphs that represent agent interactions, Federated Model Orchestration that dynamically selects the most appropriate model for each task, and an AI Economics layer that weighs execution cost against quality, latency, and governance objectives. The framework introduces a system-level perspective for measuring and improving enterprise-agentic workflows, and it shifts the unit of optimization from an isolated prompt or a single model response to the coordinated ecosystem. The paper proposes a set of architectural metrics covering workflow efficiency, model utilization, token efficiency, execution latency, reasoning quality, and operational cost, and discusses benchmarking methodologies for comparing enterprise AI systems across diverse deployment scenarios. The paper is a conceptual contribution: it specifies a reference architecture and an evaluation design, it reports no measured results, and it identifies the empirical validation of the framework against fixed-model baselines as the necessary next step.