Mefta Sadat from Loblaw Digital discusses Alfred, an agentic orchestration layer designed to run AI shopping agents reliably in production. He covers the architecture built with LangGraph and GCP, the role of the Model Context Protocol (MCP) in simplifying API interaction, and practical MLOps strategies for observability, cost management, and ensuring reliability.
Bhuvaneswari Subramani details the "Agentic Shift" in AI by presenting an evolutionary journey through seven foundational system design patterns. The talk progresses from simple conversational clients to sophisticated, multi-agent systems, covering key patterns like Retrieval-Augmented Generation (RAG), Self-Correcting RAG, and the Model Context Protocol (MCP), explaining how each pattern adds new layers of context, action, and autonomy.
This talk provides a practical framework for product managers to move beyond simple "vibe checks" to implement rigorous, data-driven evaluation for LLM-powered products. Using a live demo of a multi-agent AI trip planner, the speaker breaks down essential methodologies, including human feedback, code-based checks, and LLM-as-a-judge systems, and demonstrates how to iterate on both prompts and the evals themselves to ensure consistent quality and build user trust.
An inside look at AlwaysCool.ai's journey from simple GPT wrappers to a production-ready agentic infrastructure. This talk covers the evolution from synchronous tools to asynchronous, multi-step flows orchestrated by LangGraph, the critical role of OpenTelemetry for compliance and observability, and the architectural patterns of using FastAPI to serve centralized AI agents.
Gal Peretz introduces CodeAct, a paradigm where LLMs generate and execute Python code for tool interaction, offering a more flexible and powerful alternative to traditional JSON-based function calling for building complex, production-ready AI agents.
Sherwood Callaway of 11X discusses the architecture of "Alice," an AI Sales Development Representative. He covers the practical decision to use LangGraph for its reliability in production, the challenges of infrastructure and observability when using hosted agent platforms, and their methodology for running Evals to mitigate hallucinations by comparing generated content against source data.