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White paper
When Agents Join Your Pipeline
Agentic workflows are rewriting the assumptions CI/CD was built on. Five AI-driven changes are worth taking seriously. Learn how to manage them without breaking what already works.
How has agentic AI affected your DevOps workflows?
Agents now write and review code, open pull requests at agent speed, trigger builds, run tools, and work around the clock. If your organization has adopted coding agents in any serious way, your pipeline is already likely experiencing changes, whether you know it or not.
In this white paper, we’ll discuss what’s changing, what to do about it, and what not to do.
Change 1: Throughput
One engineer working with agents produces the commit volume of a small team now. Traditional CI stages are quietly turning into the rate limiters for AI-augmented development.
Change 2: Cost
Agent-driven CI spends on two metrics at once: compute, because there are a lot more runs, and inference, because every agent action consumes tokens. A pipeline whose cost was originally a rounding error now becomes a real line item.
Change 3: Identity
Every pipeline assumption about identity would have a person behind it. But now the questions have become: which agent produced this change, on whose behalf, with which tools, and what was it allowed to touch?
Change 4: Observability
Developers used to babysit their builds, but agents don’t. They react to failures (retry, patch, rerun) at speed, and sometimes wrongly. An agent that “fixes” a failing test by deleting it produces a green checkmark.
Change 5: Placement
For a decade, build compute was fungible: a pool of interchangeable runners somewhere. Agent workloads are ending that due to a wide range of privacy, sovereignty, capability, and scale reasons.