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A one-line fix shouldn’t wait on the whole test suite. Yet a typical CI pipeline runs every test on every push, and a single flaky failure sends developers back to the rerun button. CircleCI’s Smarter Testing addresses both problems. Test impact analysis tracks which source files each test executes, then runs only the tests affected by a code change. Auto rerun retries flaky tests inside the job instead of failing the build.

The CircleCI CLI brings CI into your terminal, where developers and coding agents can work with it directly. You can set up projects, trigger and monitor pipelines, investigate failures, retrieve test results and artifacts, manage project settings, and more. Agents can use the CLI through shell commands or its built-in MCP server, giving them the tools and context to validate changes, diagnose failures, and try again without switching to a browser.

Software teams are taking on more work than ever. Developers are juggling more branches, coding agents are keeping CI busy through the weekend, and builds are passing more often and finishing faster. But is all that extra activity helping teams ship faster? Today we released the 2026 State of Software Delivery Q3 Pulse report, the latest in our quarterly series tracking how software teams are adapting to AI-driven development.

Large pull requests can be a pain to review. When a change touches dozens of files and adds hundreds of lines of code, reviewers have a lot to work through before they can approve it. And if something needs to change, the whole PR goes through another round of review. Stacked PRs let developers split a large change into smaller, dependent pull requests that can be reviewed separately.

Intent-driven development (IDD) is an approach to AI-assisted software development where teams make the desired behavior, constraints, and criteria for success explicit, then give an agent freedom to determine how to achieve the result. As agents take on larger and more autonomous development and maintenance tasks, the implementation itself becomes easier to replace. The important question is whether the software still behaves the way the team intended.

Improving agent experience (AX) is one thing. Keeping it good as your product changes is harder. A renamed field, different error response, or overlapping tool can turn a workflow that worked yesterday into extra retries, wasted tokens, or human intervention. CI gives teams a way to catch AX regressions as part of the development process. You can test the interfaces agents depend on, run representative agent workflows against product changes, and preserve fixed failures as regression cases.

A software factory is a system for turning engineering intent into production software through repeatable, increasingly automated workflows. It connects the tools, infrastructure, people, agents, validation, and delivery controls required to move work from an idea or issue into production. Coding agents have made the model newly relevant. Teams can now produce software changes much faster than traditional development processes were designed to evaluate and coordinate.

Language servers give editors structured, real-time feedback: diagnostics, hover docs, autocomplete, and other guidance that would otherwise surface later. Language servers already exist for many of the languages and tools developers use every day, but Claude Code doesn’t automatically receive their feedback.

Testing consumes up to 50% of CI/CD pipeline time. That’s the single most expensive place in your delivery pipeline, and a meaningful share of that time is often wasted running tests unaffected by the change. Every time a developer pushes a commit, the full test suite runs. A two-line bug fix in auth.py triggers 2,000 tests, including 1,900 that don’t touch anything near auth.py. Those unrelated tests are overwhelmingly likely to pass, but the pipeline still spends 20 minutes running them.

Summary: An API defines how software interacts with a service. MCP defines a standard way for AI applications to discover and invoke tools exposed by a service. They usually work together: an MCP server can sit in front of APIs you already run, turning low-level operations into capabilities an agent can find and use at runtime. Your API may already expose everything an AI agent needs. The harder problem is helping the agent figure out which operations matter for the task it has been given.