AI Automation
Workflows that ship. Pipelines, scrapers, agents glued to APIs, and the operational discipline that keeps them running past the demo.
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.
Google Cloud TechHow Cars24 Scaled Operations with AI Agents and Internal Tooling
Cars24 integrated OpenAI APIs and Codex to automate customer journeys and internal workflows, resulting in 1M+ monthly AI-handled conversation minutes and an 80% reduction in service turnaround time.
Using ChatGPT Work to Automate Sales Workflows
ChatGPT Work integrates fragmented sales data from CRMs and communication tools to accelerate the creation of account briefs, meeting prep, and deal strategy, while keeping human judgment at the center of the process.
Redesigning Telecommunications: Deutsche Telekom's AI-Native Strategy
Deutsche Telekom is transitioning to an AI-native operating model by redesigning core workflows—rather than just automating them—across customer service, network operations, and voice communications.
Building Reliable Computer-Use Agents with Cua Driver
Cua Driver enables background AI agent operation by interacting with OS accessibility layers instead of hardware cursors, increasing task pass rates by 18% while reducing token usage.
AI EngineerAutomating Short-Form Video Production with Reelful
Reelful is an iOS app that uses AI agents to transform raw camera roll assets into polished social media content, automating scriptwriting, voiceovers, and video assembly for time-constrained creators.
Building Real-Time Industrial Digital Twins with AI
Modern digital twins must move beyond static dashboards to active, predictive systems that simulate and anticipate factory operations using real-time streaming data.
Real-Time Fluid Monitoring for Data Center Cooling Efficiency
Omen AI is using real-time optical spectroscopy to detect bacterial growth and component wear in data center liquid cooling systems, preventing costly, multi-hour system shutdowns.
Scaling E-commerce Item Knowledge with LLM-Centric Architectures
JD.com's Oxygen AIIC platform uses a 'Semantic Search then Discrimination' architecture and human-AI collaboration to manage tens of billions of SKUs, achieving 94.2% precision in automated item knowledge production.
Building an Autonomous PR Outreach Agent with OpenAI Agents SDK
Learn to build a multi-agent system in Python using the OpenAI Agents SDK to automate product research, journalist identification, and the creation of personalized PR pitches.
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