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10月7日

星期三 · 12 条
Google Developers Blog一手信源

Build zero-trust AI agents with Google's Agent Development Kit

Building autonomous AI agents that mutate production state requires moving beyond soft system prompts to a robust zero-trust architecture. To secure Google Agent Development Kit (ADK) workflows against prompt injections and malicious execution, developers must implement hardware-backed cryptographic signatures for database writes, kernel-level sandboxing with gVisor for dynamic code, and deterministic semantic gatewa…

Google Developers Blog一手信源

How to Evaluate Live & Voice Agents in ADK

Moving live voice agents from demo to production requires rigorous, automated testing to handle the unpredictability of real multi-turn conversations. ADK now provides native live evaluation, allowing developers to test graph-based agent workflows against LLM-driven simulated users that generate actual audio via Gemini TTS. By defining evaluation scenarios and natural-language rubrics, you can automatically score aud…

Google Developers Blog一手信源

4 engineering patterns behind the strongest AI Agents Challenge submissions

The recent Google for Startups AI Agents Challenge revealed that the most successful multi-agent systems rely on foundational software engineering patterns rather than just raw model power. Winning architectures consistently implemented bidirectional MCP for seamless inter-agent communication, async event buses for parallel execution, strict unified validation for model fallbacks, and tiered routing to minimize expen…

Google Developers Blog一手信源

The Anatomy of Harness Engineering: How to Evaluate, Iterate, and Guard AI Coding Agents

While end-to-end benchmarks like SWE-bench provide broad performance scores for AI agents, they are often expensive, slow, and lack the root-cause diagnostics needed to explain exactly where an agent's logic broke down. To solve this, developers should adopt behavioral evaluations—fast, local, unit-style tests that assert on discrete intermediate actions, such as verifying specific tool calls or file modifications ra…

Google Developers Blog一手信源

Announcing ADK for Kotlin 1.0: Building Production-Ready AI Agents in Kotlin, Android, and Beyond

Google has officially released version 1.0 of the Agent Development Kit (ADK) for Kotlin, achieving full feature parity with the Python and Java ADK cores to enable idiomatic, multi-agent AI development. Built on Kotlin Multiplatform (KMP), the framework leverages Kotlin Symbol Processing (KSP) for zero-reflection, type-safe function calling, alongside advanced orchestration capabilities like human-in-the-loop workfl…

Google Developers Blog一手信源

Autonomous LLM post-training with Tunix on TPUs

The "autofinetune" project introduces an autonomous research loop that fully automates LLM post-training workflows, including Supervised Fine-Tuning (SFT) and Reinforcement Learning via GRPO. By defining boundary conditions and evaluation metrics in a single Markdown specification, developers can deploy an AI agent to iteratively edit training scripts, launch experiments, and automatically commit verified hyperparame…

Google Developers Blog一手信源

Build zero-trust AI agents that judge intent, not just syntax

This blog post explores how to transition AI agents from static, build-time security controls to dynamic runtime governance using the Gemini Enterprise Agent Platform. It highlights three primary managed defenses: Model Armor for screening edge prompts, Semantic Governance Policies for evaluating tool intent against business rules, and Agent Anomaly Detection for catching multi-turn exploits. By shifting these capabi…

Google Developers Blog一手信源

Agent Anomaly Detection, now in Private Preview on the Gemini Enterprise Agent Platform

Agent Anomaly Detection is a new, out-of-band oversight layer for the Gemini Enterprise Agent Platform that analyzes OpenTelemetry traces and tool calls to catch behavioral risks without adding runtime latency to live requests. It utilizes a multi-tiered detection pipeline—combining lightweight statistical scanning with deep LLM-based reasoning—to identify logical anomalies and policy violations grounded in the OWASP…

Google Developers Blog一手信源

Why client SDK generation belongs in the open

Google has partnered with Speakeasy to open-source their OpenAPI code generation suite under the AGPLv3 license, a strategic move prompted by the sudden shutdown of Google's previous proprietary SDK provider. The newly open-sourced suite equips developers with deterministic, multi-language SDK generators that natively support strict typing and SSE streaming, alongside tools for compiling agent-native CLIs and documen…

Google Developers Blog一手信源

Introducing Support for Local AI Models in the Antigravity SDK

The Google Antigravity SDK now empowers developers to execute offline, agentic workflows locally using models like Gemma 4 26B A4B via LiteRT. This update facilitates powerful hybrid orchestration architectures, allowing a cloud model to act as a lightweight planner while local models securely handle token-intensive tasks—like code auditing and patching—directly on-device. Furthermore, the SDK provides drop-in suppor…

Google Developers Blog一手信源

Turn your REST APIs into MCP tools with Google Cloud API Gateway

Google Cloud API Gateway now acts as a native remote Model Context Protocol (MCP) server, eliminating the need to build and maintain custom middleware to expose REST APIs to AI agents. By simply adding specific annotations (like x-google-api-management.mcp) to existing OpenAPI 3.x specifications, developers can instantly convert standard REST operations into discoverable, agent-ready tools. The gateway automatically …

Google Developers Blog一手信源

Supercharge your development with the Google Developer Knowledge API ecosystem

Google is launching the Developer Knowledge API and MCP Server in public preview. This new toolset provides a canonical, machine-readable way for AI assistants and agentic platforms to search and retrieve up-to-date documentation across Firebase, Google Cloud, Android, and more. By using the official MCP server, developers can connect tools directly to Google’s documentation corpus, ensuring that AI-generated code an…

10月6日

星期二 · 3 条
Allison一手信源

Claude Haiku 5.5 in GitHub Copilot

Claude Haiku 5.5, Anthropic’s newest lightweight model, is now generally available in GitHub Copilot. It is designed for fast, high-volume work like subagents, quick edits, and terminal tasks. In early… The post Claude Haiku 5.5 in GitHub Copilot appeared first on The GitHub Blog .

Allison一手信源

Purpose-built model for leaked secret detection

Secret protection should keep pace with the way you build software, whether you write code yourself or work with an AI agent. With our new purpose-built model, we’re bringing context-aware… The post Purpose-built model for leaked secret detection appeared first on The GitHub Blog .

10月5日

星期一 · 1 条

10月1日

星期四 · 1 条
Allison一手信源

Selected models in GitHub Copilot deprecated

As of today, October 2, 2026, we have deprecated the following models across all GitHub Copilot experiences (including Copilot Chat, inline edits, ask and agent modes, and code completions). Model… The post Selected models in GitHub Copilot deprecated appeared first on The GitHub Blog .

9月30日

星期三 · 1 条
Allison一手信源

GitHub Copilot in VS Code, September 2026 releases

This changelog covers VS Code v1.136 through v1.140, shipped throughout September 2026. September’s releases streamline agent-driven development from implementation through pull request merge. Automations handle repeatable tasks, agent merge helps… The post GitHub Copilot in VS Code, September 2026 releases appeared first on The GitHub Blog .