Operations | Monitoring | ITSM | DevOps | Cloud

CircleCI Functions are now available in beta, giving developers and coding agents a faster, more reliable way to set up CI pipelines using reusable, tested steps. Functions are the next evolution of CircleCI orbs. They make common CI tasks, like installing programming languages, configuring AWS credentials, and setting up Docker, available as ready-to-use programs that agents can discover, configure, and validate before running a pipeline.

Property-based testing is a software testing technique that checks whether a program satisfies general rules, called properties, across many automatically generated inputs. Instead of writing tests for individual examples, developers define how their code should behave and let a testing framework search for inputs that break those rules.

Automating releases, semantic version bumps, and API calls often involves configuring branch permissions and storing long-lived credentials in pipeline variables. Managing credential rotation adds an ongoing maintenance burden.

We’re excited to announce that Agentic Pipelines is now generally available across all paid Cloud Bitbucket plans. You can run agents on our cloud runners or on self-hosted Linux Docker runners, with support for more runner types coming soon. With this launch, Bitbucket Pipelines evolves from a CI/CD tool into an intelligent workflow engine that increases developer productivity by letting agents handle routine engineering tasks.

Starting new software work has never been easier. Developers can assign tasks to multiple coding agents, let them work independently, and turn their attention elsewhere while those agents write, test, and revise code. As models improve, teams have more opportunities to pursue work that previously would have waited. Getting all that work validated and merged is another matter. The 2026 State of Software Delivery Q3 Pulse report shows teams working across more branches and projects.

Running several coding agents at once is pretty straightforward. Give each a task, let them work independently, and you can make progress on multiple things at the same time. But the more agents you have working, the more changes you need to coordinate. Agents can edit overlapping code, make conflicting assumptions, or finish work that depends on changes another agent hasn’t completed.

Most teams reach for Ansible in CI/CD pipelines the same way: a shell step that runs ansible-playbook, SSH keys stored as secrets, and a growing list of manual steps nobody documented. It works until it doesn't : then the failure happens at the worst possible moment and the pipeline becomes a debugging session.

We’re refreshing the Bitbucket Pipelines experience to make it easier to read logs, find the steps you need, and navigate a pipeline run. The update focuses on the Pipelines list and Pipeline result pages. This is a visual refresh only. Your pipeline configurations, builds, data, runners, variables, deployments, and integrations stay as they are. It’s the same Bitbucket Pipelines underneath, with a cleaner, more focused view.

A decision model is a specialized AI model that evaluates inputs against predefined outcomes, returning a structured choice, classification, or score. Unlike large language models (LLMs), which handle a broad range of generative and reasoning tasks, decision models are optimized for tasks like routing requests, selecting agent actions, and evaluating AI outputs.