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Sometime in the last two years your repos quietly filled up with a new category of file. Not code, not config exactly: prompts. A.claude/skills/ directory here. A.cursor/rules/ folder there. A CLAUDE.md at the root, an AGENTS.md next to it, a.mcp.json listing the servers your agent is allowed to call. These are the things that make a coding agent useful on your codebase, and they're sprawling.

Previously, I described some core approaches to validating agent written code: feedforward and feedback techniques. Feedforward techniques are about avoiding errors up front, for example by coming up with better prompts and planning strategies. Feedback gives agents a signal that they have actually achieved a task. Feedback is a key part of common agentic patterns like Ralph loops or the /goal commands in Codex and Claude Code: keep working until some known condition passes.

How to set up blue-green deployments with CircleCI.

We’re excited to announce that immutable tags are now available for the Bitbucket Packages container registry. With immutable tags, workspace admins can set container image tags from being overwritten, moved, or modified after they’re first pushed.

In December, we shared our plans to introduce pricing for self-hosted runners. You told us loud and clear that a free option matters. Today, as Premium Runners become generally available, we are happy to share that we will continue to have a free tier, which includes the use of up to 100 self-hosted runners as part of your plan. If your team needs more scale, dedicated support, or advanced management features, you can upgrade to Premium Runners when you’re ready.

This post covers some techniques to get agents to validate their work. We’ll cover why this is important, a variety of types of checks you can employ, and mechanisms you can use to enforce them.

Modern data platforms are evolving rapidly, and Google Cloud BigQuery has become a core part of analytics, AI, and large-scale reporting architectures. Teams (including Harness) rely on BigQuery to process and analyze massive datasets, but managing schema changes in a secure, repeatable way can still be challenging.