AI & Automation Briefing - October 29, 2026
Google Opens Connected Apps Platform to Developers, Linking Third-Party Tools to Gemini and Search
Google has launched a public beta of its Connected Apps Platform, allowing U.S. developers to integrate their apps directly into Gemini and Google Search. Developers connect via a cloud-hosted MCP server or Android MCP using AppFunctions APIs, then register and submit through the Google Connected Console. Once live, they can choose whether their app appears in Search, Gemini, or both, and monitor traffic, latency, and error rates. Google already has over 50 connected apps in Gemini from partners including Adobe and Experian, with those integrations rolling out to Search over the coming weeks. For operations and automation builders working in Google Workspace, this platform represents a new distribution layer where workflows and tools can surface directly inside AI-driven search and assistant experiences, following a similar logic to how SEO shaped web visibility for the previous generation of search.
Anthropic Launches Spending Analytics Tools to Help Enterprise Customers Measure AI ROI
Anthropic has released a suite of reporting tools giving enterprise customers a detailed view of how their Claude usage translates into business output. Using internal analytics and employee surveys, managers can see what a team produced over a given period, what it cost, where workflows broke down, and how to improve results. Anthropic is also introducing shared token budgets so teams can manage spend collectively. The move responds to a real pressure: a Jellyfish report based on 276,000 developer-tool users found that spending on Claude Code and other Anthropic-based tools has grown 10x since the start of the year, now representing about 2% of total engineering cost, a figure most CFOs did not anticipate. Anthropic says it will not use the analytics data for model training by default. For anyone building or selling Claude-powered automations, these tools directly support the client conversation around proving workflow ROI and structuring usage-based pricing.
TAO Scheduler Cuts LLM Agent Latency by Targeting Tool-Call Idle Time
A new scheduling framework called TAO addresses a core bottleneck in production agentic systems: GPU resources sitting idle while an LLM session waits for an external tool to return a result. TAO works in three stages. First, it pauses sessions at tool-call boundaries and moves them to a shared waiting pool, freeing GPU capacity immediately. Second, it scores each waiting session using three factors without manual tuning: how much KV-cache credit remains (with time decay), how soon the tool is predicted to return (based on runtime hints and historical durations), and token footprint. Third, it uses an asynchronous dynamic programming step to jointly select and place sessions back onto GPUs under capacity constraints, adding under 0.12% runtime overhead. Tested across 12 model-workload-concurrency configurations using DeepSeek-V4 Flash, Qwen3-32B, and GLM-4.7, TAO reduced latency by up to 26.8%, cut mean time-to-first-token by up to 36.3%, and increased decode throughput by up to 29.5% versus leading baselines. For builders designing multi-step agent pipelines, the key takeaway is that tool-return readiness, not token generation speed, is often the binding constraint on throughput.