AWS DevOps Agent adds release management capabilities to assess code changes before production (preview) | Amazon Web Services
AWS DevOps Agent now offers release management capability in preview, reviewing code changes for release readiness and running autonomous release testing to help you ship code to production safely and with confidence.
AWS DevOps Agent adds release management capability (preview) - AWS
Discover more about what's new at AWS with AWS DevOps Agent adds release management capability (preview)
Responsible AI
Discover how AWS is committing to developing AI responsibly – to built trust, promote the safe development of AI, and act as a force for good.
Trigger.dev | Build and deploy fully-managed AI agents and workflows.
Trigger.dev is the open source platform for building AI workflows in TypeScript. Long-running tasks with retries, queues, observability, and elastic scaling.
Amazon Is Using Specialized AI Agents for Deep Bug Hunting
Born out of an internal hackathon, Amazon’s Autonomous Threat Analysis system uses a variety of specialized AI agents to detect weaknesses and propose fixes to the company’s platforms.
AI Tools Accelerates Coding, but Not Overall Software Delivery, GitLab Research Finds
GitLab's 2026 AI Accountability Report highlights an AI Paradox: although 78% of developers say they code faster, overall software delivery has not accelerated due to downstream testing and review bottlenecks and new challenges for enterprise governance and traceability.
Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl
Notes from my Thoughtworks colleagues on AI-assisted software delivery
Disruption Ahead: AWS Quietly Axing Services
Amazon is quietly deprecating Cloud9, SimpleDB, CodeCommit and more services.
“You Had One Job”: Why Twenty Years of DevOps Has Failed to Do it
Let’s start with a question. What is DevOps all about?
AI Was Made for RevOps
A new wave of GenAI and AI agents opens the door to faster, smarter, more scalable revenue teams—and higher revenue growth.
The 2025 AI Agent Index Documenting Technical and Safety Features of Deployed Agentic AI Systems
Agentic AI systems are increasingly capable of performing professional and personal tasks with limited human involvement. However, tracking these developments is difficult because the AI agent ecosystem is complex, rapidly evolving, and inconsistently documented, posing obstacles to both researchers and policymakers. To address these challenges, this paper presents the 2025 AI Agent Index. The Index documents information regarding the origins, design, capabilities, ecosystem, and safety features of 30 state-of-the-art AI agents based on publicly available information and email correspondence with developers. In addition to documenting information about individual agents, the Index illuminates broader trends in the development of agents, their capabilities, and the level of transparency of developers. Notably, we find different transparency levels among agent developers and observe that most developers share little information about safety, evaluations, and societal impacts. The 2025 AI Agent Index is available online at https://aiagentindex.mit.edu.
Trusting AI with Your Data: Safe Automation from Branch to Production
Google Cloud Next 2026: The End Of The AI Pilot Era
Google Cloud Next 2026 opened with Thomas Kurian declaring the end of the AI pilot era and Sundar Pichai comparing the enterprise refrain of last year (“Can we build an agent?”) to today’s: “How do we manage thousands of them?” Google Cloud Next ’26 answers the second question with a single product story: Gemini Enterprise […]
The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems
Agentic AI systems are increasingly capable of performing professional and personal tasks with limited human involvement. However, tracking these developments is difficult because the AI agent ecosystem is complex, rapidly evolving, and inconsistently documented, posing obstacles to both researchers and policymakers. To address these challenges, this paper presents the 2025 AI Agent Index. The Index documents information regarding the origins, design, capabilities, ecosystem, and safety features of 30 state-of-the-art AI agents based on publicly available information and email correspondence with developers. In addition to documenting information about individual agents, the Index illuminates broader trends in the development of agents, their capabilities, and the level of transparency of developers. Notably, we find different transparency levels among agent developers and observe that most developers share little information about safety, evaluations, and societal impacts. The 2025 AI Agent Index is available online at https://aiagentindex.mit.edu
The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems
Agentic AI systems are increasingly capable of performing professional and personal tasks with limited human involvement. However, tracking these developments is difficult because the AI agent ecosystem is complex, rapidly evolving, and inconsistently documented, posing obstacles to both researchers and policymakers. To address these challenges, this paper presents the 2025 AI Agent Index. The Index documents information regarding the origins, design, capabilities, ecosystem, and safety features of 30 state-of-the-art AI agents based on publicly available information and email correspondence with developers. In addition to documenting information about individual agents, the Index illuminates broader trends in the development of agents, their capabilities, and the level of transparency of developers. Notably, we find different transparency levels among agent developers and observe that most developers share little information about safety, evaluations, and societal impacts. The 2025 AI Agent Index is available online at https://aiagentindex.mit.edu
