Yohan Raju of Bright Data shows how an AI agent can combine a live Amazon listing with Reddit sentiment to produce a purchase recommendation, arguing that agents need fresh, structured web data rather than relying only on pre-trained knowledge or pre-indexed retrieval.
Sam, CTO at Gravity, argues that AI agent governance must be enforced in infrastructure rather than in prompts or policy documents. Through five live demos against a hotel-booking agent, he shows a gateway blocking destructive tool calls, preventing data exfiltration, rate-limiting runaway agents, serving cached semantic responses, and rejecting oversized prompts before any LLM call. He also covers observability, vendor decoupling via LLM routing, trimming bloated MCP tool lists, and managing shadow AI.
Thorsten Hans shows how to build MCP servers and AI agents with Spin, the open-source WebAssembly framework, and run them on Akamai Functions, a globally distributed serverless platform with no cold starts. The talk explains why WebAssembly suits edge-native AI workloads, live-codes MCP servers with wasmcp in TypeScript and Rust, deploys them worldwide with one command, and closes with a Vercel AI SDK agent backed by a Linode GPU model and memory snapshots that keep apps warm.
Jose Palafox, a GitHub Field Copilot Specialist, walks through taking a Copilot agent from a single laptop to an organization-wide, pipeline-hosted system. Topics include how the Copilot harness and CLI work, building custom agents with the agent builder, sharing them through marketplaces, orchestrating cross-repository changes, and running headless agents in GitHub Actions through GitHub Agentic Workflows, with a focus on observability and cost control.
Pat Gelsinger joins a16z's Raghu Raghuram and Guido Appenzeller to discuss how AI is reshaping chip design, why design speed alone will not fix semiconductor bottlenecks, and why power and energy capacity may ultimately limit AI growth. The conversation covers HBM and memory innovation, stacking limits, the shift from copper to optics, consolidation among AI chip startups, and what a VMware for AI agents might look like.
The panel covers OpenAI's Dots and the wave of persistent AI agents, OpenAI's release of hundreds of AI-generated math manuscripts and what it means for human mathematicians, and HackerRank's Chakra AI interviewer and its implications for fairness and evaluation. The episode closes with an explainer on mixture of experts architectures and the difference between open-weight and open-source models.