AI agents are in production, but most fail quietly: errors compound across steps, costs grow per tool call, and offline benchmarks rarely predict real-world behavior. The question is no longer "Can agents work?" but "How do we make them reliable, measurable, and efficient at scale?"

Industry practitioners and researchers tackle the hardest problems: evaluating trajectories, not single outputs; building harnesses and simulators that reflect production; and optimizing agents via SFT and RL on traces, prompt and policy search, and cost-aware routing.

We ground it in recommendation systems, a natural test case. When systems understand goals and take actions, recommendation shifts from predicting clicks to helping users achieve objectives. Will agents replace the retrieve -> rank -> recommend stack, or rebuild it?

Discussion Arc - 50 min + 10 min Q&A
1. Agents in the Wild - 12 min
- What blocks deployment: reliability, compounding errors, tool misuse, cost, or safety?
- What is "solved in research" but unsolved in production?

2. Evals, Harnesses, and Optimization - 18 min
- How do we evaluate trajectories when success isn't a single label?
- Can simulated users close the offline-online gap?
- What moves the needle: base models, SFT/RL on traces, or scaffolding?

3. RecSys as the Proving Ground - 20 min
- Where should agents sit in the retrieval and ranking stack?
- Can agents optimize long-term goals over engagement and stay measurable?
- How do we move from recommending items to executing actions?
- Which RecSys strengths stay essential: scale, latency, collaborative signal, A/B rigor?

Format Hook
Each segment opens with a live poll on a provocative claim (e.g. "Within 3 years, ranking models will be a tool agents call, not the product"). Panelists react, making it a debate.

Panel Composition
Moderator +4 contrasting panelists: a large-scale RecSys lead, an agent/LLM researcher, an eval expert, and a production agent builder.