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How our engineering team ran Gemini 4 Argon unattended overnight with .agents/skills and /goal mode to cut in-memory session prep overhead by 40% in long sessions and speed up cold imports by 21% in Google's open-source Python Agent Development Kit (ADK).
Apigee hybrid 1.17.0 introduces native Model Context Protocol (MCP) support, enabling the MCP data plane to run directly within your own Kubernetes cluster. This architecture ensures that AI agent request traffic and tool execution remain entirely inside your infrastructure while pulling configuration updates from the control plane. Developers can expose OpenAPI 3.0.x specifications as discoverable MCP tools, leverage existing security policies and API products, and trace end-to-end requests usi
Google OAuth Platform now supports DPoP (Demonstrating Proof-of-Possession) to sender-constrain refresh tokens, neutralizing Infostealer malware and adversary-in-the-middle attacks. By cryptographically binding long-lived refresh tokens while maintaining short-lived access tokens as standard Bearer tokens, developers achieve robust credential security without incurring high-QPS API performance overhead. Additionally, developers integrating multi-agent or Model Context Protocol (MCP) workloads ca
The google/adk-recipes repository unifies Agent Development Kit (ADK) resources into three structured layers—core/, plugins/, and contrib/—serving as a single source of truth for building AI agents. Developers can leverage reference patterns, install modular vertical skills (such as retail product search and virtual try-on) directly into their coding assistants, and deploy production-ready services across multiple languages. This cohesive structure streamlines the entire workflow from initial sc
A practical engineering guide and reference architecture for establishing continuous evaluation pipelines for multi-turn AI agents using Gemini Enterprise Agent Platform, Google ADK, and OpenInference standards.
The open-source Agent Development Kit (ADK) and gemini-3.8-live enable developers to build responsive voice agents by implementing non-blocking tools in Python. Developers can use streaming tools to yield progress updates or background execution patterns to keep conversations fluid during long-running backend queries. Additionally, configuring response scheduling behaviors such as WHEN_IDLE, INTERRUPT, or SILENT gives developers precise control over when and how tool results are delivered into t
Meet the LiteRT Tensor API: giving developers native C++ control over the model graph to unify end-to-end ML computations on heterogeneous edge hardware.
Google Research has completed the 32-bit open-source Coral NPU RISC-V architecture with the introduction of its Matrix Execution Unit, which delivers up to 1 TOPS while targeting 10 mW average power for edge inference. Alongside this architecture, the release of the v1.0 open-source IREE/MLIR compiler allows developers to take models from JAX, PyTorch, and TensorFlow straight to Coral NPU hardware without writing custom assembly. Developers can now prototype and deploy these multimodal edge appl
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