Predictive AI forecasts what will happen next based on historical data, while Generative AI creates new content by asking what something could look like. This summary explores their fundamental differences in outputs, data types, underlying models like transformers and diffusion systems, and how they can be used together in enterprise applications.
The Arize team shares lessons from building their AI agent, Alyx, which analyzes its own trace data. They detail their journey from failed attempts like naive truncation and summarization to a successful strategy combining head/tail preservation with a retrievable memory store and using sub-agents to manage context complexity.
A product engineer from Granola shares a candid account of the challenges in moving AI features from the playground to production. This talk covers the pitfalls of "one-shot" solutions like web search and generic prompts, and details Granola's strategy of building custom internal tracing and development tooling to create a tight, effective feedback loop for iteration.
Leonie Monigatti from Elastic provides a practical guide to agentic search, arguing that effective context engineering is not just a retrieval problem, but a search problem. The workshop explores the trade-offs between specialized tools (like semantic search) and general-purpose tools (like shell and SQL execution), offering a "low floor, high ceiling" framework for building a robust and efficient retrieval stack for AI agents.
A practical guide on improving LLM coding agent performance by giving them direct access to a library's source code. The session demonstrates cloning the Effect repository to extract patterns and guide the agent in building a type-safe application from scratch.
Agent failures are unlike traditional software failures. This workshop provides a practical framework for monitoring production agents, moving beyond evals to real-world observability by using explicit signals (errors, latency) and implicit signals (user frustration, refusals, self-diagnostics) to catch regressions and understand agent behavior.