A @UiPath AI agent inside Maestro Flow took 10.6 seconds. TypeSafe AI Jev took 3.9 seconds.

A @UiPath AI agent inside Maestro Flow took 10.6 seconds. TypeSafe AI Jev took 3.9 seconds. Then I noticed something much more important than latency. The agent was 3x slower. THE PROBLEM ☕ Card disputes are high-volume and repetitive. Most cases do not need deep reasoning, but we still often send every case through either: • A human analyst • Or a full LLM agent Both are expensive ways to answer relatively simple decisions. THE BUILD 🧩 I created one UiPath Maestro Flow with two decision paths. 1️⃣ TypeSafe AI Jev through a simple HTTP call 2️⃣ UiPath AI Agent with the same input and output schema Same 10 disputes. Same task. Same cloud environment. Then I measured them. THE RESULTS 📊 ⚡ Jev: 3,913 ms per dispute 🤖 AI Agent: 10,624 ms per dispute The AI agent was 3x slower. Jev processed the batch for roughly $0.000042 per dispute. But speed was not the biggest lesson. THE PART I DID NOT EXPECT 💡 I had created a rule: Confidence < 0.60 → Human Review Sounds reasonable. Except Jev returned 1.00 confidence on 8 out of 10 cases. My confidence gate almost never had a chance to fire. The workflow was still safe because uncertain cases eventually routed to a human. But the benchmark exposed a weakness in my architecture, not the model. THE CASE THAT GOT INTERESTING 🎯 One customer said: "There is a $42 charge I don't recognize, but my son sometimes uses my card." Jev: fraud_unauthorized → 0.75 calibrated probability AI Agent: no_valid_basis → 0.62 self-reported confidence That distinction matters. One number is designed to represent probability. The other is an LLM estimating its own confidence. MY TAKEAWAY 🧠 This is not: "Small model beats AI agent." Agents are powerful when the situation requires actual reasoning. But if the question is predictable and structured, why pay LLM latency for reasoning you do not need? Classifier for predictable decisions. Agent for ambiguous reasoning. UiPath Maestro decides which one runs. That orchestration layer is the most interesting part of the experiment. 🔗 Complete flow, agent and setup guide: https://t.co/KttyViKh2q 🎥 YouTube walkthrough: https://t.co/ROvHHhjKV7 Are you still paying an agent to make decisions a calibrated model can already answer? 👇 #UiPath #Jev #TypeSafeAI #AIAgents #AgenticAI #MaestroFlow #EnterpriseAI #1AIFanatic