Build production-ready AI agents on AWS by addressing hallucinations, loops, context overload, tool failures, and observability with practical safeguards.
Build an open-source voice assistant with the Gemini Live API. Explore WebSocket audio streaming, ephemeral tokens, tool calling, privacy, and costs in Dash.
What can the latest frontier AI models actually do beyond benchmarks? The Neuron's Corey and Grant discuss AI coding agents, security risks, and why CPUs could play a bigger role in AI.
Learn how to add plain-English filters to Couchbase SQL++ with jev() and cb_decide(), returning typed answers for filtering, ranking, and classification.
Learn how Kestra unifies enterprise workflow orchestration across IT scheduling, data pipelines, infrastructure automation, business processes, and AI agents.
Wiring a Spring Boot application to a LLM running on your own machine - two ways to do it, what's the latency and how the tool calling actually looks like.
AI agents are powerful because they can autonomously plan and use tools. Sandboxes provide guardrails by isolating agents and restricting what they can access or execute.
An example that embeds DuckDB inside Neo4j as a custom Java procedure, letting DuckDB SQL queries run directly from Cypher, with results flowing back into the graph.
Five identity patterns every AI agent in production needs: own identity, scoped delegation, ephemeral credentials, per-tool authorization, provenance chain.
Learn how to monitor agents and models using Cloud Trace, Cloud Logging and Cloud Monitoring to track metrics around usage, token consumption and runtime activity.