AI Automation
Workflows that ship. Pipelines, scrapers, agents glued to APIs, and the operational discipline that keeps them running past the demo.
Standardizing Distributed AI Workflows with SAREF Ontologies
The article proposes an ontology based on the Smart Applications REFerence (SAREF) standard to enable interoperability and orchestration of AI workflows across edge, fog, and cloud computing environments.
Governing AI Skills: Scaling Agentic Workflows
AI-native organizations must treat 'skills' as first-class, governed assets—similar to microservices—to avoid technical debt, ensure deterministic outcomes, and maintain security at scale.
AI EngineerDiffusing AI into Real-World Services Businesses
AI adoption in services requires moving beyond demos to 'co-designing' technology with operators. By acquiring businesses and embedding AI directly into their workflows, builders can create real-world evals, close the feedback loop, and earn the right to move from co-pilots to autonomous co-workers.
Avoiding Disaster When Vibe-Coding Billing Engines
Use AI agents to accelerate setup in test environments, but maintain a human-in-the-loop for production billing logic to avoid runaway spend and configuration errors.
Rapid Prototyping and Deployment with Google AI Studio
Use Google AI Studio's build mode to generate, iterate, and deploy full-stack web applications via natural language prompts, bypassing manual coding for initial scaffolding.
Building and Deploying Full-Stack AI Apps with Firebase
Learn to build, secure, and deploy a real-time, full-stack to-do application using Google AI Studio and Firebase, leveraging automated authentication and real-time database synchronization.
Building and Deploying Turn-Based Web Games with AI
Learn to build real-time, turn-based web games using event sourcing, Firestore for state synchronization, and Google AI Studio for iterative debugging and deployment.
Scaling AI Evals via Cross-Functional Ownership
DoorDash’s GenAI platform team scaled evaluations by moving from an engineering-only task to a cross-functional workflow, using stable APIs and 'vibe-coded' UIs to empower non-engineers to own quality.
Building uReview: Scaling AI Code Review at Uber
Uber built uReview, a multi-agent code review engine, to solve the bottleneck of increasing PR review times. By focusing on observability, team-specific customizations, and feedback-driven tuning, they achieved a 60% cost reduction and a 67% addressal rate for AI-generated comments.
Building Context Engines for AI Agents
AI agents fail at complex tasks because they lack organizational context, leading to 'satisfaction of search' errors. A context engine provides intent, conventions, and historical data, reducing token waste and preventing compounding logic errors.
AI EngineerThe Agentic Commerce Stack: Building Reliable AI Shopping
Agentic commerce is shifting from brittle browser-automation to standardized protocols like ACP and UCP. To build reliable shopping agents, developers must move away from DOM-scraping toward structured product feeds, standardized tool access (MCP), and rigorous behavioral evals to prevent production failures.
Scaling Engineering Capacity Through AI-Assisted Self-Service
By integrating Codex into internal workflows, loveholidays empowered non-engineers to build products and manage infrastructure, resulting in a 73% increase in deployment frequency and shifting engineering focus toward higher-level platform improvements.
Building and Scaling Multi-Agent AI Systems on GKE
A practical guide to deploying AI agents on GKE, using the Model Context Protocol for infrastructure troubleshooting, and implementing secure sandboxing for AI-generated code.
Google Cloud TechScaling Go-To-Market Teams with Agentic Workflows
Justin Joyce of Cloudflare explains how to scale GTM operations by replacing manual spreadsheet analysis with a three-pillar agentic framework: skill-based data querying, automated insight delivery, and a self-service agentic workspace.
Radar: Making Podcast Audio Discoverable for AI Agents
Radar is a podcast search engine and API that transcribes and indexes audio, enabling AI agents to process spoken content, track entity mentions, and analyze advertising trends.
Building Blocks of Go-to-Market Orchestration
Go-to-market orchestration is about moving from manual, siloed campaigns to describing intent and having agents execute across channels. The key is building a unified data substrate and solving narrow, vertical use cases before scaling horizontally.
Engineering a Unified GTM System at Notion
Notion unified its fragmented GTM operations by treating them as a distributed systems problem, building a shared context layer where humans and AI agents operate on the same substrate to drive proactive, signal-based workflows.
GTM Engineering: Building a Technical Foundation for Growth
GTM engineering treats go-to-market operations as a software engineering problem, focusing on data resolution, complex orchestration, agentic decision-making, and execution to build a 'perfect virtual copy' of the market.
Streamlining Workspace Administration with the Admin Plugin
The new Admin plugin for ChatGPT Work and Codex allows administrators to analyze data and execute management tasks directly within a chat interface, eliminating the need to switch between disparate tools.
4 Common Loop Engineering Failures and How to Fix Them
Loop engineering automates repetitive tasks by setting goals and retrying, but it often fails due to runaway costs, confirmation bias, vague objectives, or excessive complexity. Success requires strict stop rules, external evaluation, concrete metrics, and transitioning to graph-based architectures for complex workflows.
Google Cloud TechBuilding Agentic Platforms: The Potter's Workshop Approach
Safia Abdalla argues that AI agent platforms should abstract infrastructure complexity, provide consistent multi-harness support, and act as 'potter's workshops'—structured, observable systems that empower humans to ship software rather than just automating code production.
AI EngineerBuilding an Agent Kernel: Why Frameworks Fall Short
Instead of using complex agent frameworks, build a simple 'kernel' that treats agents as isolated processes. Use content-addressed prompts, event-driven architecture, and strict type boundaries to ensure reliability and auditability.
Scaling AI Engineering: From Solo Prompts to Systemic Automation
AI-powered development scales not through individual prompting, but by building reusable harnesses and system-level context that reduce human intervention and standardize engineering practices across teams.
Model Routing: Moving Beyond Leaderboard Benchmarks
Stop relying on a single 'best' model. Use a task-aware router to dynamically select models based on your specific cost, latency, and quality preferences, achieving comparable results at a fraction of the cost.
Solving Alignment Bottlenecks in Chip Design with AI
In high-stakes industries like chip design, alignment is a quadratic cost that outweighs individual skill. A shared nervous system—using a living graph of intent and role-specific agents—can reduce communication overhead and prevent costly errors.
FinOps for AI Agents: Implementing Run-Level Token Governance
Token Ops introduces a control plane that manages AI agent costs at the run-level using 'steering'—injecting instructions to reduce token consumption—rather than just hard-capping or killing processes.
Give AI Agents a Budget, Not a Token
Stop giving AI agents 'god tokens' with unbounded power. Instead, treat them like junior engineers by enforcing budgets through asymmetric verbs, rate limits, trip wires, and the 'undo test' to bound their blast radius.
Human Judgment in the Age of AI Software Factories
AI agents accelerate code generation, but they don't replace the need for human taste. A 'software factory'—a repeatable, event-driven loop—is the best way to encode engineering culture and quality gates while focusing human attention on high-risk decisions.
Scaling Product Marketing with AI-Driven Knowledge Systems
Stampli reduced product launch timelines by 3.16x by using AI to centralize product context, automate content production, and provide real-time data analysis.
Ramp Enters AI Infrastructure with 'Router' API
Corporate expense platform Ramp has launched 'Router,' an API service that enables companies to switch between multiple LLM providers, leveraging three years of internal infrastructure development.
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