A software factory is an AI-driven development system that coordinates agents, tools, and automated workflows to turn requirements into working software. Rather than relying on engineers to manually carry out each step, a factory can take an issue through planning, implementation, testing, and review until a pull request is ready.
More autonomous implementations can also merge and deploy changes with minimal human intervention. But even as more of the development process becomes automated, teams still need to decide how those changes reach users, under what conditions, and with what safeguards.
As software factories mature and increase delivery throughput, release decisions could become harder to manage at the same pace. Though deploys have become routine, releases have not.
Release decisions shape how every change ships
The distinction between deployment and release matters because completing a change doesn't establish how it should reach users. Writing code determines what software can do, but releasing it determines who experiences the change, in what order, and under what conditions.
These decisions differ from those made during development because they depend on how software behaves in production, where real traffic, user behavior, and system dependencies can reveal problems that tests and code reviews didn't catch. A change can be technically correct and still introduce unexpected latency, increase infrastructure costs, or disrupt a particular group of users.
That uncertainty makes release decisions an ongoing process rather than a one-time approval. Teams must decide whether to start with internal users or a small group of customers, what production signals justify expanding access, and when to pause or reverse a rollout.
Today, engineers and release owners often make these choices through individual judgment, approvals, and release meetings, monitoring the results as exposure increases. These practices provide oversight and accountability, but they also require people to interpret production evidence and determine how much risk to accept at each stage.
Factories amplify weaknesses in the delivery pipeline
That reliance on human judgment becomes more difficult to sustain as the systems producing changes become more autonomous. CI/CD made it possible to move changes from commit to production through increasingly automated pipelines.
Software factories extend that automation into planning, implementation, testing, and review, allowing agents to carry work across stages that previously required human intervention. But the road to a reliable delivery pipeline has always been strenuous, and the factory doesn't shorten it.
Rather than simply accelerating an existing pipeline, a software factory can run multiple development workflows in parallel, producing changes at a higher rate.
That increased throughput can expose weaknesses in the underlying engineering process, from flaky tests and inconsistent review standards to environments that drift apart. A reliable process becomes more productive at scale, while an unreliable one can generate failures just as efficiently.
The same increase in throughput also changes the demands placed on release management. Every merged change carries a decision about how and when it should reach users, whether a team makes that decision deliberately or defaults to exposing the change as soon as it deploys.
As factories increase the volume of changes, the number of release decisions grows alongside them, while the human attention available to evaluate each one may remain unchanged. The resulting bottleneck matters, but the greater risk lies in decisions that go unmade. When deployment automatically means release, a change can reach every user at once, with no independent way to reverse its exposure short of another deploy.
Release decisions govern what leaves the factory
Addressing that risk doesn't require slowing down software production. It requires separating the rate at which changes are produced from the rate at which they reach users. Just as manufacturing capacity and product distribution operate at different rates, software development throughput and release exposure can be managed independently.
By encoding release decisions into plans, teams can define how changes progress through environments, audiences, and rollout stages, regardless of whether an engineer or an agent merged the code.
But controlling exposure is only useful if teams can respond to what happens after a change reaches production. Unlike distributing a finished physical product, releasing software also creates new evidence about how a change behaves under real-world conditions.
Release plans can use that evidence to determine when exposure should expand, pause, or reverse, while reserving human judgment for decisions that require additional context. The factory can continue producing changes at speed without requiring every change to reach every user at the same pace.
This is where LaunchDarkly fits into the software delivery process, bringing feature flags, AI Configs, guarded and progressive rollouts, and observability together in a runtime control layer that helps teams manage how changes reach users and respond to their impact.
