A deep dive into building and scaling production-ready AI agents, detailing a model-driven approach using the open-source 'Strands' SDK and a cloud-native architecture for deploying remote tools with MCP and AWS Lambda.
Barr Yaon of Amplify Partners presents early findings from the 2025 State of AI Engineering survey, covering LLM usage, customization techniques like RAG and fine-tuning, the state of AI agents, key challenges like evaluation, and community perspectives on the future of AI.
Authors James Phoenix and Mike Taylor discuss the evolution of prompt engineering from a creative art to a rigorous engineering discipline. They cover the core principles of prompting, the importance of programmatic evaluation, the role of agents, and how to manage application lifecycles as models evolve.
A deep dive into the challenges of building production-grade, low-latency voice AI agents, and how the open-source, vendor-neutral framework Pipecat provides a comprehensive solution for development, deployment, and scaling. Learn about voice AI architecture, the trade-offs between speech-to-speech and text-based models, and practical deployment strategies.
Ivan Nardini from Google Cloud demonstrates how to build, enhance, and productionalize AI agents using Google Cloud's agent stack. The session covers the challenges of deploying agents and introduces the Agent Development Kit (ADK) for building, the Vertex AI Agent Engine for managed deployment, and protocols like MCP and Agent-to-Agent for tool integration and inter-agent communication, using Claude on Vertex AI as the core LLM.
Paul van der Boor and Sean Kenny from Prosus detail the journey of Toqan, an internal AI platform that evolved from a Slack experiment into a sophisticated agentic system. They share insights on driving enterprise adoption, key metrics for measuring productivity, and their future vision of an "AI Workforce" where employees architect AI agents to automate complex, cross-system tasks.