Building upon the AgentFlow framework, which introduced a no-code programming paradigm through natural language prompts, this paper addresses the critical challenge of its practical deployment and resource efficiency. AgentFlow features a-conductor. that enables true parallelism and deterministic execution, yet assumes a homogeneous environment in which every agent is executed by a Large Language Model (LLM). We propose a significant evolution of the conductor into an orchestrator with intelligent workload distribution, capable of dynamically assigning agent execution to the most appropriate and cost-effective backend: LLMs for tasks requiring deep reasoning, compact LMs or Small Language Models (SLMs) for moderate tasks, or deterministic code functions for simple, well-defined operations. This extension preserves the core no-code philosophywhere all logic resides exclusively within promptswhile introducing a new layer of execution intelligence that dramatically reduces resource consumption without sacrificing functionality. We present the formal model, architecture, and implementation of this enhanced conductor, demonstrating its ability to optimize the-cost-performance. curve for complex agent swarms. Keywords: No-Code Programming, LLM, SLM, Agent Swarm, Orchestrator With Intelligent Workload Distribution, Agentflow, Heterogeneous Execution