Uploaded June 2026 | Updated September 2026, 2 weeks ago
Traditional software testing relies on deterministic matching: run the same input, expect the exact same output. AI agents break that assumption completely, which is why they can pass every demo and still fail once real users show up.
This is the first video in Arize AX: Getting Started, where we build a real financial-analysis agent and take it from a working demo to production-grade over the course of the series.
Watch this to learn:
• Why AI agents have no fixed "expected output," and why that breaks traditional testing
• How evals let you catch regressions, switch models safely, and ship with confidence
• The AI lifecycle and the three pillars this series is built on: observe (traces), evaluate (evals), and improve (the loop)
Chapters:
00:00 Building AI Systems That Actually Work
00:46 Running a Query Three Times is Not a Test Suite
01:20 Why Traditional Unit Tests Fail for LLMs
01:31 The System Prompt Whack-A-Mole Problem
02:01 Model Switching: Flying Blind vs. Flying by Instruments
02:25 How Descript, Bolt, and Claude Code Scale AI Testing
02:42 The AI Development Lifecycle
03:12 The 3 Pillars of Arize AX: Observe, Evaluate, Improve
03:40 Why Evals Are the Connective Tissue of AI Software
Once you can define what "good" looks like, you can start catching failures before your users do.
👉 Sign up for free: app.arize.com/auth/login?utm_source=youtube&utm_medium=organic_social&utm_campaign=arize_ax_getting_started
🔗 Learn more about Arize AX: arize.com
📓 Colab notebook for the series: colab.research.google.com/drive/1dViThD0kJjbqtDGE7-ciBYIRwV-HBN1T
📚 Docs: docs.arize.com
🔔 Subscribe for weekly content on LLMs, agents, and evaluation: youtube.com/@arizeai?sub_confirmation=1
#ArizeAX #AIAgents #LLMEvaluation
Traditional software testing relies on deterministic matching: run the same input, expect the exact same output. AI agents break that assumption completely, which is why they can pass every demo and still fail once real users show up.
This is the first video in Arize AX: Getting Started, where we build a real financial-analysis agent and take it from a working demo to production-grade over the course of the series.
Watch this to learn:
• Why AI agents have no fixed "expected output," and why that breaks traditional testing
• How evals let you catch regressions, switch models safely, and ship with confidence
• The AI lifecycle and the three pillars this series is built on: observe (traces), evaluate (evals), and improve (the loop)
Chapters:
00:00 Building AI Systems That Actually Work
00:46 Running a Query Three Times is Not a Test Suite
01:20 Why Traditional Unit Tests Fail for LLMs
01:31 The System Prompt Whack-A-Mole Problem
02:01 Model Switching: Flying Blind vs. Flying by Instruments
02:25 How Descript, Bolt, and Claude Code Scale AI Testing
02:42 The AI Development Lifecycle
03:12 The 3 Pillars of Arize AX: Observe, Evaluate, Improve
03:40 Why Evals Are the Connective Tissue of AI Software
Once you can define what "good" looks like, you can start catching failures before your users do.
👉 Sign up for free: app.arize.com/auth/login?utm_source=youtube&utm_medium=organic_social&utm_campaign=arize_ax_getting_started
🔗 Learn more about Arize AX: arize.com
📓 Colab notebook for the series: colab.research.google.com/drive/1dViThD0kJjbqtDGE7-ciBYIRwV-HBN1T
📚 Docs: docs.arize.com
🔔 Subscribe for weekly content on LLMs, agents, and evaluation: youtube.com/@arizeai?sub_confirmation=1
#ArizeAX #AIAgents #LLMEvaluation
