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Google ADK: Introduction to AI Agent Development

Nota: ✋ This post was originally published on my blog wiki-cloud.co Introduction Artificial intelligence is evolving at an unprecedented pace and is transforming how people and businesses interact with technology. Over the past few years, much of the focus has been on generative AI models, which can create text, images, code, audio, and other types of content from natural language instructions. These capabilities have marked a significant and transformative shift in how we perform many tasks, allowing AI to move from a specialized technology to an accessible tool for millions of users. However, we are entering a new stage. Artificial intelligence models are no longer limited to simply answering questions or generating content. They can now be autonomous, understand objectives, analyze context, decide what steps to take, use tools, consult different sources of information, connect with APIs, execute actions, and collaborate with other specialized agents to complete more complex tasks. This evolution is giving rise to what is known as agentic artificial intelligence, an approach in which AI systems can act with a greater level of autonomy and actively participate in business, technical, and operational processes. Instead of simply offering a recommendation, an agent can search for information, validate data, coordinate different activities, and execute a sequence of actions aimed at achieving a specific goal. Within this new scenario appears Google Agent Development Kit , also known as Google ADK , is an open-source framework developed and designed by Google to facilitate the creation, evaluation, and deployment of artificial intelligence agents. ADK provides developers with a structure for defining agent behavior, connecting them to language models and external tools, managing sessions and memory, coordinating multi-agent systems, and evaluating their performance before deploying them to production. Thanks to this code-based approach, Google ADK allows you to build e

2026-07-26 原文 →
AI 资讯

I thought giving my group chat AI assistant Google Calendar would take 5 minutes, and then OAuth humbled me

I went looking for a simple answer to a simple question: How do you give an agent access to Google Calendar? Not a demo. Not a screenshot. A real agent, running unattended, with enough access to be useful and enough guardrails that it won’t turn into a security incident. While researching OpenClaw setups, I found a thread on r/openclaw where someone asked what looked like a tiny question: what do I need to add Google Calendar to OpenClaw? One reply said: "Look into gog cli." That answer is way more revealing than it looks. Because the hard part usually isn’t Google Calendar itself. The hard part is everything hidden behind the phrase "connect Google" . And if you’re building agents in n8n, Make, Zapier, OpenClaw, or a custom OpenAI-compatible loop, auth is only half the problem anyway. Once the workflow runs 24/7, you also need to think about retries, quota limits, caching, and how many LLM calls the thing is quietly making in the background. That’s where a lot of teams hit the same wall: the integration works, but the operational shape of it is bad. Security is fuzzy. Request volume is noisy. And AI costs get weird fast if every poll and retry triggers more model calls. The demo version is lying to you If you’ve used something like n8n Cloud, you’ve seen the polished version: Click Google Calendar Sign in Approve access Done That flow is real inside a managed product. But the minute you leave the managed garden — self-hosted n8n, OpenClaw, a custom MCP server, a Python worker on Ubuntu, or your own app using the OpenAI SDK against an OpenAI-compatible endpoint — you inherit the boring parts. Now "connect Google" actually means: create a Google Cloud project configure the OAuth consent screen choose the right OAuth client type enable the Google Calendar API pick the right scopes store credentials safely handle refresh tokens deal with quota errors later That’s not setup trivia. That’s infrastructure. One user in that same OpenClaw discussion realized it immediately:

2026-07-26 原文 →
AI 资讯

Google Apps Script Quota Limits 2026 — Every Error, Every Fix

If your automation just stopped mid-run, you have hit a quota limit. Here is exactly which one and how to fix it — free, no upgrade required, works at any order volume. Quick answer — the numbers that matter in 2026: Both consumer (free) and Google Workspace accounts get a 6-minute maximum execution time per script run — the old 30-minute Workspace limit no longer applies. Consumer accounts are capped at 90 minutes of trigger runtime per day; Workspace accounts get 6 hours of trigger runtime per day. UrlFetch calls are capped at 20,000 per day on consumer accounts (100,000 on Workspace). These are the hard limits that cannot be increased — they are why Autocrat, Sheets automations, and document workflows fail at scale. If you have ever seen “Service invoked too many times”, “Exceeded maximum execution time”, or “Could not obtain lock”, you already know the symptom. This guide explains the exact numbers behind those errors, when they appear by account type, and what actually works when order or document volume gets serious. What Are Google Apps Script Quotas? Google Apps Script quotas are hard limits on how much work a script can do. They exist to protect shared infrastructure — stopping one workflow from consuming resources that affect thousands of other users. Quotas appear across four layers, and they all apply simultaneously: User-level quotas — tied to the Google account running the script. Consumer and Workspace accounts have different ceilings. Project-level quotas — tied to the Apps Script project itself. Concurrent executions are capped here. Service quotas — Gmail, Sheets, Drive, UrlFetch, and other services each have their own daily limit. Execution quotas — limits on how long a single run can take and how much total runtime is consumed in a day. The critical point is that these limits stack independently. A workflow can be fine on execution time but fail on service call rate — which is why the same script can work perfectly for a small operation and break

2026-07-26 原文 →
AI 资讯

Google basically confirms the Pixel 11 is getting a price hike

Google's Vice President of Devices and Services, Shakil Barkat, all but confirmed in an interview with 9to5 Google that its next Pixel phone would cost more than the Pixel 10. Considering the ongoing RAM supply issues due to the explosion of AI data centers, the rumored price hike is not a complete surprise. Companies from […]

2026-07-26 原文 →
AI 资讯

You can’t ignore Google Zero anymore

The web and Google once had a deal: Google collects data and indexes webpages and in exchange sends oceans of traffic to websites. The deal wasn't perfect and certainly made Google more money than it made the websites, but it worked for a long time. Now, however, the deal seems to be dead. And the […]

2026-07-25 原文 →
AI 资讯

Zero to Multi-Region: High Availability Serverless with Cloud Run and Cross-Region Failover & Failback

Google just made multi-region Cloud Run significantly easier. Here is the full picture; what changed, what it means in practice, and how to build it right. Most teams discover they need multi-region architecture the hard way and sadly, during an outage. Whether you're running a global e-commerce platform, a real-time gaming API, or a financial services application, users expect your service to be available whenever they need it. There is a conversation that happens in almost every engineering team at some point. It usually starts with a post-mortem. A regional Google Cloud outage or a Cloud Run service that hit a cold start spike, or a single-region deployment that could not handle the latency demands of users spread across Lagos, Nairobi, and London simultaneously, caused enough pain that someone finally asked: why are we only deployed in one region? The answer is usually one of three things: it felt complex, it felt expensive, or no one had prioritised it yet. In July 2026, Google moved Cloud Run Service Health to General Availability and the timing was hard to miss. Six days earlier, a power cut at Google's Netherlands data centre had knocked three services offline. The GA release brings automatic cross-region failover to Cloud Run with what Google describes as a two-step setup: add a readiness probe, set minimum instances to at least 1. The load balancer does the rest. This article covers the full architecture, what Service Health is, how readiness probes underpin it, how to set up the Global Load Balancer correctly, and how to test that failover actually works. It also covers the production details. What changed: Service Health and readiness probes Before Service Health, multi-region Cloud Run required you to implement a /health endpoint in your application and configure a separate HTTPS health check at the load balancer level. This worked, but it had a significant gap. The load balancer's health check only knew whether the Cloud Run service endpoint was respon

2026-07-24 原文 →
开发者

Google hit with $1 billion fine for breaking EU antitrust rules

The European Union has fined Google's parent company Alphabet €890 million (about $1 billion) for two separate violations of the bloc's Digital Markets Act (DMA). One penalty is for giving its own products preferential treatment in search results, while the other is for blocking Android developers from sending users to alternate payment options. A €460 […]

2026-07-23 原文 →