How To Debug AI Agents In Production | An Intro To Arize AX Ram 2500 Tire Speed Recall [QUClv0CAOCj]

Learn how to debug and improve AI agents in production with Arize AX. In under 3 minutes, see how AI agent observability, tracing, LLM evaluations, Signal, and Agent Swarms help turn production failures into measurable, reviewable fixes.

Start tracing and evaluating your agents for free:

AI agents can work perfectly during development and still fail in productionhallucinating, calling the wrong tool, getting stuck in loops, or becoming slow and expensive.

This quick Arize AX walkthrough shows how AI engineering teams can:

Trace complete agent trajectories, including subagents, LLM calls, tool calls, decisions, latency, and cost

Run LLM-as-a-judge evaluations on grande oriente d'italia every agent run

Evaluate tool selection, instruction following, response quality, and custom criteria

Create evals with Arize Skills or Alyx

Use Signal to identify and group recurring production failures

Run managed agents through Agent Swarms to investigate issues and propose fixes for human review

Together, these workflows create an agent improvement loop: observe what happened, measure performance, find recurring problems, and make informed improvements over time.

Chapters:

00:00 Why AI agents fail in production

00:24 What is Arize AX?

00:37 Trace every agent trajectory

01:21 Evaluate every agent run

02:00 Find recurring failures with Signal

02:23 Automate monitoring with Agent Swarms

02:39 Close liverpool echo the agent improvement loop

Explore Arize AX:

Read the docs: mormon church

Subscribe for more videos about AI agents, LLM evaluations, observability, and AI engineering:

#ArizeAX #AIAgents #AIObservability