cloudflare·Aug 2, 2026·2 min readCurated summary

Welcome to Agents Week

The post introduces “Agents Week” by reframing the question from what an Agent Cloud should be to what agents themselves need. Unlike today’s human-oriented cloud and web, an Agent Cloud must provide agent-native primitives while also translating between existing human-designed systems and an emerging agent-shaped web. The week will explore how infrastructure, development, security, organizational access, and web interactions should evolve for agents. ## Why the Existing Cloud Falls Short - Today’s cloud and web assume a human is monitoring and interacting with them. - Interfaces are built around attention, visual dashboards, clicking, reading, and human decision-making. - Agents have different priorities: speed, structure, reliable access, and the ability to operate continuously without fatigue or distraction. ## Two Responsibilities of an Agent Cloud - Build an agent-native foundation with primitives designed for agents from the beginning rather than adapted from human tools. - Serve as a translation layer between: - The existing human-shaped web and infrastructure. - The agent-shaped systems being developed for the future. ## Topics for Agents Week The series will examine: - Storage, compute, and execution primitives required by agents. - An agentic development lifecycle, or “ADLC,” that reduces or removes humans from routine development loops. - Secure ways for employees and agents to access organizational systems of record and perform meaningful work. - The evolution of the agentic web, including discovery, access, and payments. - How humans and agents will work together during the transition to agent-oriented systems. ## Invitation to Ask Agents Directly The post encourages readers to ask their own agents what they need from an Agent Cloud instead of relying only on human assumptions. Suggested areas include cloud infrastructure, development processes, secure enterprise access, and web interactions, with readers invited to share their agents’ responses. In practical terms, designing an Agent Cloud requires starting with agents’ operational needs while supporting compatibility with the human-oriented systems that still dominate today.

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SdlcAI AgentsCloud ComputingAgent Cloud+2

gitlab·Jan 15, 2026Original article

Introducing GitLab Credits (opens in new tab)

GitLab is transitioning from seat-based pricing to a usage-based model with the introduction of GitLab Credits, a virtual currency designed for the GitLab Duo Agent Platform. This shift addresses the limitations of traditional licensing, which often creates "AI haves and have-nots" by making access too expensive for light or occasional users. By pooling resources across an entire organization, GitLab aims to provide equitable access to agentic AI for every developer while ensuring costs align with actual consumption. ## The Shift from Seat-Based to Usage-Based AI * Traditional seat-based models are poorly suited for agentic AI, which can be triggered by background SDLC events rather than just direct user interaction. * The credit model allows every member of a Premium or Ultimate organization to use AI capabilities without requiring an individual "AI seat." * Usage-based pricing automatically offsets the costs of power users against lighter users, lowering the total cost of ownership for the organization. ## Mechanics of GitLab Credits * Credits function as a pooled resource consumed by both synchronous interactions (like Agentic Chat in the IDE) and asynchronous background tasks. * Supported capabilities include foundational agents (Security, Planner, Data Analyst) and specific workflows such as Code Review and CI/CD pipeline fixing. * The system integrates with external models like Anthropic Claude Code and OpenAI Codex, as well as custom agents published in the GitLab AI Catalog. * Each credit has an on-demand list price of $1, with volume discounts available for enterprise customers who sign up for annual commitments. ## Governance and Usage Controls * Administrators can monitor consumption through two dedicated dashboards: a financial oversight portal for billing managers and an operational monitoring view for administrators. * Granular controls allow organizations to enable or disable Duo Agent Platform access for specific teams or projects to prevent unexpected credit depletion. * Proactive email alerts are triggered when consumption reaches 50%, 80%, and 100% of committed monthly credits. * A sizing calculator is available to help organizations estimate their monthly credit requirements based on patterns observed during the platform's beta period. ## Transitioning and Promotional Access * Existing GitLab Duo Pro and Duo Enterprise customers can roll over their current seat investments into GitLab Credits with volume-based discounts. * As part of a limited-time promotion, GitLab is providing $12 in monthly credits per user for Premium subscribers and $24 per user for Ultimate subscribers. * Self-managed and GitLab Dedicated customers will gain access to these credit-based features starting with the 18.8 and 18.9 releases. For organizations looking to scale AI across the software development lifecycle, the credit-based model offers a more flexible and cost-effective path than rigid seat licenses. Current Premium and Ultimate subscribers should leverage their monthly promotional credits to baseline their usage before committing to larger annual credit bundles.

SdlcAI AgentsMachine LearningGitlab Duo+4