Uploaded April 2026 | Updated September 2026, 3 weeks ago
In this deep-dive session, Google Cloud and Arize AI break down what it really takes to build, evaluate, and operate AI agents in production.
From evaluation frameworks and context engineering to observability, governance, and real-world deployment—this talk covers the full lifecycle of modern AI agents.
If you’re building agents (or planning to), this is packed with practical insights you won’t want to miss.
⏱️ Timestamps:
00:00 – Welcome & overview of the event
02:01 – Agenda: building → evaluating → deploying agents
03:31 – Evaluation + optimization loop explained
06:52 – Eval framework: build, test, analyze
08:06 – Generating test data with simulations
09:50 – LLM-as-a-judge & evaluation metrics
13:08 – Context engineering fundamentals
15:21 – From demo agents → production challenges
18:51 – Model improvements & tradeoffs
19:55 – Tool hardening for reliability
22:35 – Context compaction (reduce tokens & cost)
25:05 – Multi-agent design & functional isolation
29:26 – Handling failures with circuit breakers
32:26 – Arize: observability & continuous improvement
45:29 – Demo: evaluating & improving agents
01:09:31 – Running agents in production (Google Cloud)
01:22:09 – Why governance & monitoring matter
01:31:03 – Tracing, debugging & observability deep dive
01:43:56 – Final takeaways & closing
#AI #LLM #Agents #MachineLearning #MLOps #GenAI #GoogleCloud #ArizeAI
In this deep-dive session, Google Cloud and Arize AI break down what it really takes to build, evaluate, and operate AI agents in production.
From evaluation frameworks and context engineering to observability, governance, and real-world deployment—this talk covers the full lifecycle of modern AI agents.
If you’re building agents (or planning to), this is packed with practical insights you won’t want to miss.
⏱️ Timestamps:
00:00 – Welcome & overview of the event
02:01 – Agenda: building → evaluating → deploying agents
03:31 – Evaluation + optimization loop explained
06:52 – Eval framework: build, test, analyze
08:06 – Generating test data with simulations
09:50 – LLM-as-a-judge & evaluation metrics
13:08 – Context engineering fundamentals
15:21 – From demo agents → production challenges
18:51 – Model improvements & tradeoffs
19:55 – Tool hardening for reliability
22:35 – Context compaction (reduce tokens & cost)
25:05 – Multi-agent design & functional isolation
29:26 – Handling failures with circuit breakers
32:26 – Arize: observability & continuous improvement
45:29 – Demo: evaluating & improving agents
01:09:31 – Running agents in production (Google Cloud)
01:22:09 – Why governance & monitoring matter
01:31:03 – Tracing, debugging & observability deep dive
01:43:56 – Final takeaways & closing
#AI #LLM #Agents #MachineLearning #MLOps #GenAI #GoogleCloud #ArizeAI
