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. CircleCI’s 2026 State of Software Delivery found that the typical team’s feature-branch activity rose 15% last year while throughput on main fell almost 7%.

Producing more code does not improve delivery if verification becomes the bottleneck. A software factory therefore needs CI that can keep pace with the rate of change, running required tests and policy checks independently before code can merge or deploy.

This post explains how a software factory works, how coding agents are changing its design, and what CI needs to handle as development volume increases.

The stages are familiar from the software development lifecycle: plan work, write code, test it, review it, deploy it, and monitor production. A software factory connects those activities so routine work can move between them without depending on developers to coordinate every handoff manually.

Teams still decide how software should be built and released. The difference is that those decisions are encoded into the development system wherever possible: how work gets assigned, which environments and tools are available, which checks are required, when human approval is necessary, and how failures get routed back for another attempt.

Most software factories bring together the same core functions:

Automation is especially valuable at the boundaries between those functions. An issue tracker might assign a bounded task to an agent, for example, while CI can return enough detail about a failed test for the agent to continue working. Later in the lifecycle, monitoring and incident systems can turn production problems into new engineering work.

Software factories existed long before coding agents. Agents change the amount of development work a team can produce and how quickly it arrives.

A developer can now hand off a bounded implementation task instead of performing every step manually. Agents can inspect a codebase, modify files, invoke tools, run tests, investigate failures, and revise their work before asking for human input.

Developers can also run several streams of agent work concurrently. As adoption grows, CI sees more commits, more pull requests, more test executions, and more repair attempts without a corresponding increase in engineering headcount.

Eventually, the systems downstream of code generation have to absorb the extra work.

If agents produce changes faster than CI can validate them, CI becomes the bottleneck. More agent capacity simply creates a longer queue.

Other bottlenecks can appear just as quickly:

• full test suites run for every small change, consuming time and compute
• limited executor capacity leaves builds waiting to start
• developers have to interpret routine CI failures before agents can continue
• security checks serialize work that could otherwise run concurrently
• deployment approvals require manual action even for routine, low-risk releases

A software factory has to optimize end-to-end throughput. Faster implementation only translates into faster delivery when testing, security, CI, review, and deployment can keep up with the additional work.

Throughput is only half of the problem. Teams also need to know that faster, more automated development has not weakened the checks used to decide what can ship.

Developers and agents can run tests locally while they work, but they also control their development environments and can change which checks they run. Required CI jobs create a separate boundary where the organization controls the environment, test suite, security checks, and policies applied to a proposed change.

CI can then produce the information other parts of the development system need. A failed run can go back to an agent with enough context to investigate the problem, while a successful run can satisfy repository rules or allow a deployment workflow to continue once any required approvals are complete.

As agents take on more implementation work, the speed and reliability of those CI checks directly affect how much of their work can make it through integration.

Running every agent edit through a full remote CI pipeline would create another bottleneck. Agents need fast checks while they work, followed by broader independent validation when a change is ready for integration.

Inner-loop checks need to be cheap and fast enough to run repeatedly. Depending on the change, an agent might compile an affected component, run the relevant tests, perform type checking, or inspect a concise failure report before preparing the work for integration.

CircleCI brings CI feedback closer to the development environment through its CLI and isolated Chunk sidecar environments. Developers and agents can validate working-directory changes without turning every attempt into a commit followed by a full pipeline run.

Catching problems earlier reduces unnecessary CI work and gives agents useful feedback while they still have the task context needed to respond.

Once a change is ready for integration, CI runs the broader checks the organization requires.

The code executes outside the agent’s development environment, with the organization controlling which test suites, security gates, and policies apply. Teams can also preserve test results, artifacts, and approval history alongside the change.

Inner-loop validation shortens the iteration cycle. Outer-loop validation provides the independent result used for merge and deployment decisions.

Agent-generated workloads change both the scale CI has to handle and the way developers and agents interact with it. Higher concurrency cannot come at the expense of useful failure information, required checks, or traceability.

Several capabilities become especially important:

• High concurrency: Agent activity can arrive in bursts, so CI needs enough execution capacity to keep queue time low.
• Efficient testing: Test selection and intelligent splitting reduce unnecessary work while keeping feedback fast.
• Isolated execution: Required checks should run outside the environment where the developer or agent produced the code.
• Useful failure output: Agents need enough detail to identify what failed and determine what to try next.
• Policy gates: Security, approval, and deployment requirements should be enforced as part of the workflow.
• Provenance and traceability: Test results, security checks, artifacts, and approvals should remain associated with the change they apply to.
• CI observability: Teams need visibility into queue time, test duration, failure rates, recovery time, throughput, and cost.

Pipeline speed still matters, but the larger requirement is keeping CI responsive and reliable as the volume of work increases.

Higher change volume multiplies the amount of test work CI has to perform. Parallelism can shorten a full suite, but it does not change how much of the suite runs in the first place.

Selective testing provides another lever. Instead of treating every feature-branch change as if it affected the entire codebase, CI can identify which tests are relevant and reserve broader runs for the points in the workflow where they are needed.

CircleCI’s Smarter Testing capabilities address both problems. Intelligent Test Selection identifies tests affected by a feature-branch change, while test splitting distributes the tests that do run across parallel containers using historical timing data.

Using both approaches can reduce the time and compute required to reach a useful CI result, which becomes increasingly important as agents generate more test runs.

Agents and CI jobs often need access to package registries, cloud environments, deployment systems, and other infrastructure. More automated work means more workloads requesting access, which makes identity and credential management especially important.

Where possible, teams can use short-lived authentication such as OIDC instead of distributing long-lived cloud credentials. Secrets that still need to be stored should have least-privilege access, with restrictions on which projects, jobs, and teams can use them.

Security policy also belongs in CI. Required scans can block changes that violate policy, while provenance and recorded approvals give teams a traceable history of what was built, checked, and released.

Many CI workflows still rely on a developer to interpret a failed build. Someone opens the job, searches the logs, identifies the relevant error, decides whether the problem is reproducible, and figures out what to try next.

Putting an agent into the development workflow does not help much if every CI failure still requires the same manual translation step. Agents need direct access to the job, test, and failure information required to continue working.

Useful failure context might include:

• the failed job and step
• the affected test
• relevant error output
• previous test behavior or flake history
• current workflow state
• links or identifiers needed to inspect related CI data

The CircleCI MCP server exposes pipeline status, build failure logs, test results, flaky-test information, reruns, and other CI operations to coding agents. The CircleCI CLI brings similar capabilities into the development environment.

With those interfaces available, an agent can investigate a CI failure and attempt a repair without requiring a developer to operate the dashboard on its behalf.

CircleCI provides the CI and validation infrastructure needed to keep developer- and agent-generated work moving through the factory without weakening the checks around it.

CircleCI calls the broader model autonomous validation. An agent can run targeted checks while it works, then send a proposed change through the required CI jobs in controlled infrastructure. When CI finds a problem, the agent can retrieve the relevant test results or failure logs, make another attempt, and run the checks again.

Once the required jobs pass, the team’s existing merge, approval, and deployment policies remain in control of what happens next. Humans stay involved where the team wants human judgment rather than being pulled into every routine failure or handoff.

No. Organizations ran software factories long before coding agents existed, and the model does not depend on them.

Agents increase the amount of software work a team can produce without increasing headcount at the same rate, so testing, security, and CI have to handle more changes more frequently.

Teams adopting agents should increase CI and test capacity alongside them. Give agents a fast inner-loop check they can run while they work, enforce security requirements in CI, and use test splitting to keep required checks fast as test volume grows.

Most engineering organizations already have many of the required pieces: issue trackers, CI/CD, developer platforms, coding agents, security tools, observability, deployment infrastructure, and review processes. The practical work is usually in the connections between them.

Start with a real change and trace it from task creation to production. Pay attention to the places where work stops: a developer copying information between systems, an agent waiting for missing context, a build sitting in a CI queue, or a routine failure that still requires someone to dig through logs before development can continue.

A few questions can expose the biggest opportunities:

• Where are developers still coordinating routine handoffs manually?
• Which checks are too slow to run as often as agents need them?
• Where is CI already approaching its concurrency or queue-time limits?
• Can an agent understand and act on a failed CI run without human help?
• Which security or deployment policies still depend on someone remembering to apply them?

Address the bottlenecks in order of impact. Bring fast validation closer to developers and agents, keep required CI checks independent, increase concurrency where queues form, expose useful failure data to agents, and encode security and deployment policy into the workflows every change already passes through.

A software factory succeeds when the entire delivery system can turn engineering work into validated, production-ready software at a higher rate, rather than simply producing more code at the front of the process.

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