Handing an AI agent real access to your systems is an infrastructure decision, not a productivity hack. These posts cover securing MCP servers with OAuth 2.1 Dynamic Client Registration, sandboxing agents behind container and VM-level isolation, keeping credentials out of AI coding assistants, and building the feedback loops that make autonomous agents recoverable when they go wrong. Alongside them: grounding LLMs in executable code, and the code-review patterns that make model output verifiable in CI rather than merely plausible.
The 2026-07-28 MCP revision deprecates RFC 7591 Dynamic Client Registration in favour of Client ID Metadata Documents, and deletes initialize, sessions and ping. A correction to my January post, with what I changed in a real server.
Stop credential leaks through AI coding assistants with ephemeral credentials, automated redaction, and audit trails. Battle-tested patterns for production.
Master container isolation for AI coding agents. Transform security risks into production-ready DevOps tools with Docker, Kubernetes, and proven patterns.
Cut through AI code review hype. Learn proven patterns for LLM-based reviews that improve velocity without sacrificing quality. Start smarter today.
Deploy fast, privacy-first AI code completion with local models. Master training, optimization, and production patterns. Start building today.
Master production AI agent feedback loops with automated monitoring, error recovery, and observability patterns. Deploy reliable autonomous agents at scale today.
Deploy secure code execution at the edge with Cloudflare Sandbox SDK. Production patterns for AI agents, interactive environments, and CI/CD pipelines.
Dynamic Client Registration in OAuth 2.1 transforms MCP server security and data governance. Learn implementation patterns for enterprise AI deployments.
An in-depth technical analysis of how Cloudflare Sandbox SDK enables production-grade LLM code execution with VM-level isolation, streaming feedback, and edge deployment. From architecture to implementation patterns.
Deep dive into adversarial attacks on production LLM systems. Learn data poisoning vectors, detection strategies, and hardening techniques for robust AI security at scale.
How reducing tool sprawl, consolidating workflows, and building intelligent automation can double your effective productivity in DevOps.
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