AI Automation
Workflows that ship. Pipelines, scrapers, agents glued to APIs, and the operational discipline that keeps them running past the demo.
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.
Integrating AI Agents into Event-Sourced Systems
Improve fraud detection by layering agentic AI onto existing event-sourced architectures, using a semantic layer to provide agents with the necessary context to resolve ambiguous transactions.
Securing the AI Supply Chain: The Skill Vector Approach
To mitigate supply chain risks in a regulated environment, treat AI skills like software dependencies by implementing a hybrid deterministic and LLM-based vetting pipeline before they reach an internal marketplace.
AI EngineerSimulationMaxxing: Shipping AI Agents 20x Faster
By replacing manual or production-based evaluation with grounded, synthetic simulations, teams can iterate on AI agents in hours rather than weeks, effectively short-circuiting the traditional release bottleneck.
Building Autonomous Software Factories with Forward Deployed Engineering
Forward deployed engineering is shifting from manual consulting to building 'software factories'—autonomous systems where AI agents handle the full lifecycle from signal to deployment, provided the codebase is 'agent-ready' with robust validation loops.
AI EngineerScaling Forward Deployed Engineering with AI Agents
Varick Agents scales bespoke enterprise automation by building 'Forward Deployed Agents' that act as assistants to human engineers, allowing them to map, re-engineer, and deploy workflows on top of existing legacy systems without requiring migrations.
Building Complex Apps with Claude Code and Dynamic Workflows
Claude Code's new dynamic workflows allow developers to automate complex, multi-step coding tasks by generating deterministic, parallelized JavaScript execution plans that can be saved, edited, and reused.
Automating Performance Engineering with AI Agents at Netflix
Netflix uses AI agents to bridge the gap between profiling data and production code fixes, creating a self-improving catalog of performance anti-patterns that allows for automated, canary-validated optimizations.
Applying Control Theory to AI Coding Agents
Instead of using AI agents to generate massive, unreviewable pull requests, use control theory to build iterative loops that make small, verifiable, and incremental code changes.
AI EngineerBuilding Private Agent Benchmarks from Production Traces
To reliably ship AI agents, companies must move beyond public benchmarks and build private, simulation-based CI pipelines that replay production traces in controlled, repeatable environments.
Evaluating AI Video: Moving from Absolute Scores to Pairwise Comparison
To solve for temporal incoherence and 'vibe-based' evaluation failures in AI video, Character.ai replaced absolute scoring with a pairwise preference model trained on a small VLM, enabling automated quality gates in the generation loop.
Building Closed-Loop Evals for Multimodal Agents at Scale
Uber's food photography enhancement agent uses a multi-stage, closed-loop evaluation system that combines offline human-labeled benchmarks with automated self-correction and production feedback loops to maintain quality and faithfulness at scale.
AI EngineerAutomating Incident Response with Self-Improving Agents
Observability is shifting from passive dashboards to active telemetry for AI agents. By feeding production traces directly into code-aware sandboxes, teams can automate root cause analysis and generate pull requests for fixes.
Mastering AI-Driven Workflows with Codex
Jason Liu demonstrates how to transform AI agents from simple chatbots into persistent, autonomous teammates by leveraging memory vaults, cross-thread communication, and multi-modal context tools like Appshots.
How News Organizations Are Integrating AI into Editorial Workflows
News organizations are deploying AI to automate repetitive tasks, unlock value from massive archives, and create personalized reader experiences, ultimately allowing journalists to focus on original reporting.
Securing Multi-Agent Systems with Model Armor
Protect multi-agent systems from indirect prompt injection, PII leaks, and malicious content by implementing Model Armor as a centralized security guardrail at every system boundary.
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