Not Another AI Podcast Ep. 08: Here's When You Actually Need an Agent (And When You Don't)

Here's When You Actually Need an Agent (And When You Don't) Everyone is talking about AI agents, agentic architecture, and autonomous workflows. But does every AI workflow actually need an agent? In this episode, Piyanka Jain, Shanti Greene, and Anshul Chawla break down when to use AI agents, when a traditional pipeline or machine learning model is better, and how to build AI systems that are reliable, efficient, and cost-effective. They explore: • What actually makes an AI system "agentic" • When deterministic workflows beat AI agents • How LLMs can accelerate developers and data scientists without replacing core ML systems • Why you should freeze working ML pipelines instead of rebuilding them with an agent • Agent architecture, orchestration, evaluation loops, and self-correction • How poor model selection can cause massive LLM token costs • Why an eval harness is essential for AI model selection and production systems • How to choose the smallest and cheapest model that can solve the task • AI sovereignty and the growing case for open-source and on-premise AI models • How companies can balance AI performance, reliability, and cost The core takeaway: Use agentic AI when you genuinely need a non-deterministic workflow. Otherwise, use AI as an accelerator and keep the underlying system deterministic. If you're building AI agents, LLM applications, enterprise AI systems, or AI-powered products, this conversation offers a practical framework for deciding where agents actually add value. Topics: AI Agents, Agentic AI, AI Architecture, LLMs, Machine Learning, AI Costs, Token Optimization, Eval Harnesses, AI Model Selection, Enterprise AI, Open Source AI, AI Sovereignty. #AIAgents #AgenticAI #ArtificialIntelligence #LLM #AIArchitecture #MachineLearning #GenerativeAI #EnterpriseAI #AIEngineering #LLMOptimization