Operations | Monitoring | ITSM | DevOps | Cloud

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.

AI agents can write code faster than any developer. But for most teams, the feedback loop hasn’t kept pace. The agent generates code, pushes it to CI, and minutes later a full pipeline run catches a simple linting error or a failing unit test. By then the agent has moved on. Getting back to a working state means rebuilding context from scratch and burning tokens just to fix something that should never have shipped in the first place.

AI coding agents produce code faster than most teams can validate it. Without a validation step between the agent and CI, every problem gets caught after the push, and feedback arrives long after the agent has lost context. Agents need consistent feedback while they’re working so that small failures get fixed locally and CI stays focused on moving code into production.