AI Automation
Workflows that ship. Pipelines, scrapers, agents glued to APIs, and the operational discipline that keeps them running past the demo.
Querying and Acting on Cloud Data with Data Agent Kit
The Data Agent Kit provides a unified framework of MCP servers, agent skills, and IDE integrations that allow AI agents to securely query, analyze, and modify data across BigQuery, Cloud SQL, and Cloud Storage.
Google Cloud TechBuilding Resilient Web Data Infrastructure for AI
AI systems require live, reliable data pipelines. Success in this space is not about building once, but maintaining an 'adapt forever' architecture that handles extreme scale, latency, and anti-bot measures.
AI EngineerThe Economics of Web Context: Renting vs. Owning for AI Agents
For high-frequency AI knowledge work, renting context via APIs becomes prohibitively expensive. Building an owned data pipeline often reaches a cost-efficiency tipping point at surprisingly low volumes (around 15,000 queries).
Practical Loop Engineering for AI Agents
Loop engineering uses autonomous feedback cycles to automate repetitive tasks. By combining 'goal' primitives for bounded tasks and 'loop' primitives for scheduling, developers can build reliable agentic workflows while maintaining human oversight for critical judgment.
Travis Kalanick on Industrial AI and the Future of Physical Systems
Travis Kalanick argues that the next industrial revolution will be driven by 'physical AI'—using software, robotics, and sensors to automate massive, overlooked industries like mining, food production, and logistics.
Scaling AI-Native Development: Lessons from RingCentral
RingCentral accelerated product development and internal operations by sponsoring an 'AI-Native Challenge,' empowering employees to build with AI tools while keeping humans in the loop for verification and strategy.
Thrive Holdings Raises $2B to Scale AI-Integrated Enterprises
Thrive Holdings, an OpenAI-backed firm, is scaling its 'private equity for AI' model by acquiring traditional businesses and embedding AI workflows to improve efficiency in accounting, IT, and infrastructure.
Building an Automated LLM-Powered Knowledge Base
Transform disorganized raw notes into a structured, interconnected wiki using voice dictation, LLM-based enrichment, and automated cloud-based pipelines.
Building Production-Ready AI Agents with Claude Managed Agents
Anthropic's 'Claude Managed Agents' abstracts the complex infrastructure of agentic loops—session management, sandboxing, and observability—allowing developers to focus on domain-specific logic rather than production plumbing.
AI EngineerHow Virgin Atlantic Uses ChatGPT Work to Accelerate Product Strategy
Virgin Atlantic leverages ChatGPT Work to consolidate fragmented customer data and automate competitive research, reducing weeks of manual analysis to hours while improving cross-team decision-making.
Scaling Marketing Operations with Autonomous AI Workflows
Zapier’s enterprise marketing team uses ChatGPT Work to automate lead quality assurance and campaign execution, resulting in seven-figure pipeline growth and reduced manual reporting.
Building an AI-Native Finance Function: Lessons for CFOs
Redesigning finance around AI requires moving beyond simple automation to building interactive, real-time decision-support tools that empower finance professionals to own the full lifecycle of data-driven insights.
Optimizing Finance Workflows with GPT-5.6 Sol
Model ML uses GPT-5.6 Sol to automate the 'last mile' of finance work, reducing token usage by 36% in Excel and 21% in PowerPoint while significantly increasing professional-readiness rates for automated deliverables.
Decoupling RL Rollout Fleets from Training Clusters via Stitch
By exploiting the fact that Adam-optimized model updates are sparse in low-precision serving views, you can sync rollout weights via 500MB patches instead of 500GB checkpoints, enabling global, elastic RL training.
AI Engineer5 Best Practices for Building Reliable AI Agent Skills
AI agent skills are procedural knowledge files. To make them reliable, focus on precise triggers, domain-specific expertise, context efficiency, deterministic scripts for fragile tasks, and rigorous security vetting.
Running AI Agents in Production Without the On-Call Tax
Engineering teams spend 70% of their time on operational overhead rather than coding. By deploying autonomous background agents that leverage production context, teams can automate incident triage, deployment monitoring, and routine operational tasks, effectively offloading the 'on-call tax'.
AI EngineerBuilding Agentic Workflows and Real-Time Multiplayer Development
GitHub Next is moving beyond AI-assisted typing to automate the 95% of software engineering that isn't coding, focusing on agentic workflows defined in Markdown and real-time collaborative environments.
AI EngineerScaling AI in Professional Services: The HSP GRUPPE Approach
HSP GRUPPE transformed its operating model by integrating AI not as a productivity shortcut, but as a core organizational capability, resulting in 40,000+ hours of reclaimed capacity annually.
How Rippling Cut AI Costs by 63% While Maintaining Usage
After discovering that AI token consumption was on track to consume 90% of its R&D budget, Rippling built an AI Spend Console to route prompts to cost-effective models and measure individual employee ROI.
Cloudflare Launches Kitesurf: A Headless Browser for AI Agents
Cloudflare has introduced Kitesurf, a cloud-hosted, headless browser built on Workers, designed specifically for AI agents to navigate the web efficiently without the overhead of traditional consumer browsers.
Naïve Raises $28.5M to Automate Autonomous Business Operations
Naïve provides an API-first infrastructure that allows AI agents to provision and manage business operations—from incorporation to cloud resources—while building specialized runtime layers to reduce the high costs of agent inference.
Secure AI Coding: A Framework for Production-Ready Agents
To use AI agents securely, treat them like junior developers: enforce small, test-driven batches, provide scoped context, use hardened sandboxing, and verify output with traditional security tooling.
Rebuilding Industrial Capability with Software-First Mining
Mariana Minerals is applying a software-first, vertically integrated approach to mining and refining, aiming to solve the critical mineral bottleneck required for modern technology and national security.
a16z (Andreessen Horowitz)Runware's Modular Pods: A Portable Alternative to Data Centers
Runware is deploying modular, transportable 'Sonic Inference Pods' to provide decentralized, waterless AI inference capacity that scales faster than traditional, fixed-facility data centers.
Scaling Telco Personalization with Multi-Agent AI Architectures
Circles transformed telco operations by using OpenAI’s API to build a multi-agent support system (CareX) and a personalization engine (Xplore IQ), resulting in a 65% autonomous resolution rate and 22% ARPU growth.
Scaling AI Adoption Through Governance and Employee Agency
Univé transformed its operations by treating AI as an organizational shift rather than an IT project, using strong governance to empower employees to build 1,500+ custom GPTs and automate complex workflows.
Scaling Retail Expertise with GPT-Realtime
avatarin deployed a 24/7 multilingual voice agent for Yamada Denki using GPT-Realtime, achieving 30,000 interactions in two weeks with a 92% positive satisfaction rate by prioritizing context-aware conversation over keyword-based chatbots.
Automating Healthcare Administration with AI Agents
Lassie is replacing manual administrative labor in healthcare practices with AI agents that handle billing, insurance, and scheduling, allowing providers to focus on patient care rather than paperwork.
a16z (Andreessen Horowitz)Dili Secures $21.7M to Automate Infrastructure Compliance
Dili uses a hybrid AI-deterministic architecture to automate complex regulatory compliance for large-scale infrastructure projects, reducing manual reporting time from days to minutes.
Scaling AI Development: Automating the Developer Loop
To scale production AI agents, developers must stop being the bottleneck by using parallel sub-agents, git worktrees, and autonomous loops to handle the end-to-end bug-fix lifecycle.
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