agents

3 posts

cloudflare

Building the agentic cloud: everything we launched during Agents Week 2026 (opens in new tab)

Cloudflare’s Agents Week 2026 introduced a broad set of infrastructure primitives for building and operating AI agents at scale. The company argues that agents require a new cloud model—“Cloud 2.0”—with elastic compute, built-in security, persistent state, specialized tools, and support for agent-driven web traffic. Its announcements span compute environments, identity and networking, developer tooling, inference, voice, email, and memory. ## Compute for Autonomous Agents - **Artifacts** provides Git-compatible, versioned storage for code and data. It supports tens of millions of repositories, remote forking, and access through standard Git clients. - **Cloudflare Sandboxes**, now generally available, give agents persistent isolated computers with shells, filesystems, and background processes. Environments can start on demand and resume where they left off. - **Outbound Workers for Sandboxes** act as programmable, zero-trust egress proxies. They let developers inject credentials and apply dynamic outbound security policies without exposing secrets to agent-generated code. - **Durable Object Facets** allow dynamically generated Workers to create isolated Durable Objects with their own SQLite databases, enabling stateful applications built on the fly. - **Workflows** was rearchitected to support up to 50,000 concurrent executions and a creation rate of 300, making it more suitable for durable, long-running background agents. ## Security, Identity, and Private Networking - **Cloudflare Mesh** provides private network access for users, infrastructure, Workers, and autonomous agents. Combined with Workers VPC, it enables scoped access to private databases and APIs without manually configured tunnels. - **Managed OAuth for Cloudflare Access** lets agents authenticate to internal applications on behalf of users using RFC 9728 rather than insecure shared service accounts. - New identity controls include scannable API tokens, improved OAuth visibility, and resource-scoped permissions to support least-privilege access and automated credential protection. - Cloudflare outlined an enterprise architecture for governing **MCP** deployments using Access, AI Gateway, and MCP server portals. - **Code Mode** reduces MCP token costs, while new Cloudflare Gateway rules help detect unauthorized or “Shadow MCP” usage. ## The Agent Toolbox - A new preview of the **Agents SDK**, called Project Think, aims to provide a more complete platform for agents that can reason, act, and persist. - An experimental **voice pipeline** supports real-time speech-to-text and text-to-speech over WebSockets, requiring roughly 30 lines of server-side code. - **Cloudflare Email Service** entered public beta, allowing agents to send, receive, and process email as a native communication channel. - Cloudflare’s AI platform is becoming a unified inference layer supporting models from more than 14 providers, including third-party model bindings for Workers and an expanded multimodal catalog. - Cloudflare also described a custom infrastructure stack for serving large language models efficiently on its global network. - **Unweight**, a lossless inference-time compression system, reduces model footprints by up to 22%, improving GPU memory efficiency and potentially lowering inference cost and latency. - **Agent Memory** was introduced as a managed service for giving agents persistent memory, though the provided article excerpt ends before detailing its full capabilities. Cloudflare’s announcements collectively position Workers and related services as a platform for the agentic cloud: one capable of running agents, securing their access, preserving their state, and supplying the models and communication tools they need to operate continuously at Internet scale.

gitlab

Monitor, manage, and automate AI workflows (opens in new tab)

The GitLab Duo Agent Platform’s Automate capabilities provide a centralized framework for managing, executing, and monitoring AI-driven development workflows within the software development lifecycle. By integrating event-driven triggers and detailed session logging, the platform allows developers to transition from manual AI interactions to fully autonomous, production-ready processes. This orchestration layer ensures that AI agents are not only performant but also transparent and easy to audit across projects. ## Resource Management for Agents and Flows The Automate hub serves as the control center for organizing AI resources, distinguishing between agents (entities that perform tasks) and flows (structured sequences of actions). * Resources are categorized into "Enabled" (those available for project use) and "Managed" (those created and owned specifically by the project). * Custom agents and flows must be enabled at the top-level group before they can be activated for specific projects. * Users can expand their automation library by browsing and enabling pre-configured resources from the GitLab AI Catalog. ## Event-Driven Automation with Triggers Triggers allow AI agents to respond automatically to specific actions within the GitLab interface, eliminating the need for manual invocation. * Automation can be initiated through three primary event types: user mentions (e.g., `@agent-name`), issue/MR assignments, or reviewer assignments. * When a trigger is activated, the system identifies the associated flow, executes the agent, and posts the final results directly back to the relevant issue or merge request. * Common use cases include using the `/assign` quick action to trigger a CI/CD optimizer or a code explanation agent. ## Workflow Monitoring and Session Transparency The Sessions interface provides a detailed audit trail for every execution, offering visibility into the "black box" of AI decision-making. * The Activity tab tracks step-by-step reasoning, showing exactly which tools the agent used and the results of individual actions. * Execution statuses are monitored in real-time, with labels such as Running, Finished, Failed, or Input Required. * The Details tab provides deep technical context by linking directly to Runner job logs, including system messages and full tool invocation outputs. ## Practical Conclusion To maximize the utility of the GitLab Duo Agent Platform, teams should move beyond experimental chat prompts and begin configuring triggers for repetitive tasks like code review assignments or issue triaging. Utilizing the Sessions tool is recommended during the initial rollout phase to verify agent reasoning and ensure that custom flows are interacting correctly with project data before full-scale deployment.

gitlab

AI Catalog: Discover, create, and share agents and flows (opens in new tab)

The GitLab AI Catalog serves as a centralized repository designed to streamline the discovery, creation, and distribution of AI agents and automated flows across an organization. By providing a structured environment for managing foundational and custom AI assets, it fosters team collaboration and ensures consistency throughout the development lifecycle. Ultimately, the catalog enables developers to scale AI-driven automation from experimental private prototypes to production-ready, instance-wide solutions. ## Discovering and Enabling AI Assets * The catalog acts as a central hub for two distinct asset types: Agents, which handle on-demand or context-specific tasks, and Flows, which are multi-step automations that orchestrate multiple agents. * Users can browse assets via the Explore menu, inspecting titles, descriptions, and visibility statuses before implementation. * To utilize an asset, it must first be added to a top-level group via the "Enable in group" button and then activated within specific projects. * The duplication feature allows teams to copy existing agents or flows to serve as templates for further customization. ## Development and Configuration * Custom agents are built by defining specialized system prompts and configuring specific tool access, such as granting read-only permissions for code and merge requests. * Custom flows utilize a YAML-based structure to define complex behaviors, incorporating components like prompts, routers, and agent hierarchies. * New assets are typically assigned a unique display name (e.g., `ci-cd-optimizer`) and initially set to private visibility to allow for safe experimentation. * Effective creation requires thorough documentation of prerequisites, dependencies, and specific use cases to ensure the asset is maintainable by other team members. ## Managing Visibility and Sharing * Private visibility restricts access to project members with at least a Developer role or top-level group Owners, making it ideal for sensitive or team-specific workflows. * Public visibility allows anyone on the GitLab instance to view and enable the asset in their own projects. * Best practices for sharing include using descriptive, purpose-driven names like `security-code-review` rather than generic identifiers. * Organizations are encouraged to validate and test assets privately before moving them to public status to ensure they solve real problems and handle edge cases. ## Versioning and Lifecycle Management * GitLab employs automated semantic versioning (e.g., 1.1.0) where any change to a prompt or configuration triggers an immutable version update. * The platform uses "version pinning" to ensure stability; when an asset is enabled, projects remain on a fixed version rather than updating automatically. * Updates are strictly opt-in, requiring users to manually review changes and click an "Update" button to adopt the latest version. * Version history and current status can be monitored through the "About" section in the Automate menu for both agents and flows. To maximize the benefits of the AI Catalog, organizations should establish a clear transition path from private experimentation to public sharing. By leveraging version pinning and granular tool access, teams can safely integrate powerful AI automations into their development workflows while maintaining full control over environment stability and security.