#automation
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OpenAI and Thailand Launch AI Accelerator for Local Startups
OpenAI and Thailand’s Ministry of Higher Education, Science, Research and Innovation (MHESI) have launched an eight-week accelerator to help ten local startups transition from prototypes to production-ready AI products in healthcare and education.
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.
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.
7 Modular Design Patterns for AI Coding Agents
Improve AI coding agent performance by replacing long, confusing prompts with modular 'skills'—specialized text files that the agent loads dynamically only when needed.
Google Cloud TechBuilding 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.
The 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 Real-Time Voice AI Agents with Gemini Live
Gemini Live enables bidirectional, audio-native conversations by using WebSockets for streaming and built-in voice activity detection to handle interruptions and tool execution.
Google Cloud TechRadar: 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.
QueryStory: Building Trust in Enterprise AI Analytics
QueryStory is a platform designed to bridge the trust gap in enterprise AI by providing transparent, verifiable data narratives and automated SQL auditing, moving beyond the 'black box' limitations of general-purpose AI agents.
Runable's $21M Bet on AI Agents for Business Growth
Runable is pivoting from AI-assisted software creation to end-to-end business growth, using AI agents to manage marketing, SEO, and customer acquisition for non-technical small business owners.
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.
Designing AI Environments for Collective Intelligence
Moving from rigid agent workflows to open, incentive-driven environments enables AI to solve complex scientific problems and optimize GPU kernels through collective, iterative collaboration.
AI Engineer4 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.
Anthropic Unifies Claude Memory Across Chat and Cowork
Anthropic has merged the memory systems for Claude chat and Claude Cowork, allowing the AI to retain context across different workflows and giving users manual control to edit or delete stored information.
AI Agents: Why the Harness Matters More Than the Model
AI system performance is driven by the 'agentic harness'—the tools, memory, and loops surrounding the model—rather than just the model itself. Distinguishing between the 'brain' (model) and the 'jar' (harness) is essential for building effective AI agents.
Terminal Agents: The State of AI in Command-Line Environments
This survey provides a comprehensive overview of AI agents designed to operate within terminal environments, detailing the architectures, evaluation methodologies, and challenges of automating command-line tasks.
OpenAI's Shift to Agentic Workflows for Non-Engineers
OpenAI is expanding beyond coding tools with 'ChatGPT Work,' an agentic platform designed to automate complex, multi-step tasks across common business software, aiming to move AI from simple Q&A to autonomous project execution.
Human Judgment in the Age of AI Software Factories
As AI agents scale development, human judgment shifts from writing code to defining intent, system design, and verification strategy. A 'software factory'—a repeatable, event-driven loop—is the framework for managing this shift, provided you balance verification budgets with human oversight.
Building 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.
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