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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.

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.

Git worktrees let you check out multiple branches of the same repository in separate directories. Each worktree has its own files and staging area, but they all share the same Git history. You can work on several branches without repeatedly switching between them, stashing changes, or cloning the repository again. Worktrees are especially useful when working with coding agents. Tools like Claude Code, Codex, and Cursor can use them to handle multiple tasks in parallel without editing the same files.

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.