Evaluating and optimizing AI agents: from observability to continuous improvement
Conference Context
- Date/time: 2026-07-01 · 1:30pm-1:50pm
- Track/room: Track M · Track M
- Speaker(s): Chang Liu
- Session type/status: sponsor · confirmed
- Track: Track M
- Room: Track M
- Session type: sponsor
- Status: confirmed
Session Description
AI agents don’t behave like traditional systems. Learn how to evaluate outputs, trace behavior, and apply a continuous loop to improve performance across prompts, tools, and models. Using signals grounded in real-world context via Foundry IQ, see how evaluation, tracing, and optimization come together to turn production usage into measurable improvements over time.
Media Evidence
No related AI Engineer channel video found yet.
Evidence Graph
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Media Signals
No linked video, transcript, or slide source has been attached yet.
Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.
Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.
People
Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.
Synthesis
Synthesized Breakdown
Evaluating and optimizing AI agents: from observability to continuous improvement ## Conference Context - Date/time: 2026-07-01 · 1:30pm-1:50pm - Track/room: Track M · Track M - Speaker(s): Chang Liu - Session type/status: sponsor · confirmed - Track: Track M - Room: Track M - Session type: sponsor - Status: confirmed ## Session Description AI agents don’t behave like traditional systems. Learn how to evaluate outputs, trace behavior, and apply a continuous loop to improve performance across prompts, tools, and models. Using signals grounded in real-world context via Foundry IQ, see how evaluation, tracing, and optimization come together to turn production usage into measurable improvements over time. ## Media Evidence No related AI Engineer channel video found yet.
Speaker And Company Context
Topics Covered
- Topic links are pending transcript-backed classification.
Derived Links And Source Material
Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.
Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.