01
Overview
Mastra Factory is built around a staged software-delivery workflow rather than a single coding chat. GitHub and Linear can feed work into the system, agents operate inside defined stages and sandboxes, and humans can review the plan, diffs and final pull request before code is merged.
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The default lifecycle
Mastra describes its own Factory board as Intake → Triage → Planning → Build → Review → Done. The stages are configurable, and each can be manual or automatic.
That distinction matters. A team can use agents for the expensive middle of the workflow without giving them unconditional authority over intake, architecture decisions or merges.
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Step 1: Intake from GitHub or Linear
Work begins as an issue or pull request from a connected source. GitHub is the natural source when work already lives beside the codebase; Linear is useful when product and engineering teams manage backlog and status there.
A sensible intake record should include a clear problem, expected behavior, relevant constraints and acceptance criteria. Agents can ask questions during triage, but ambiguous issues still create ambiguous implementation risk.
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Step 2: Agent-assisted triage
During triage, Factory can inspect the task and repository context, then surface questions or missing information. Mastra's product description explicitly keeps team members involved here: people can answer the agent's questions before work proceeds.
Use triage to reject tasks that are too broad, security-sensitive or impossible to verify automatically. Not every issue should become an autonomous build job.
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Step 3: Planning with a human checkpoint
The planning stage converts the request into an implementation approach. Mastra says team members can approve or edit plans before implementation begins.
This is one of the most valuable control points. A coding agent can produce syntactically valid code while choosing the wrong architecture. Reviewing the plan before code generation is cheaper than discovering the problem after a large diff exists.
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Step 4: Build inside a sandbox
At the Build stage, agents work in sandboxed environments. They can inspect repository files, make changes and run checks according to the rules configured for that stage.
Sandboxing reduces risk but does not erase it. The sandbox's credentials, network access and tool permissions determine what the agent can actually affect. Keep production secrets and unrelated infrastructure outside the agent's reach unless the task truly requires them.
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Step 5: Review the diff and checks
Factory keeps each work item as an agent session with chat, tool activity and workspace files. Reviewers can inspect the changes and the reasoning trail around them.
Require tests, linting and type/build checks that match the repository. Agent-written code is easier to trust when the repository has strong automated verification. Weak test coverage shifts more responsibility back onto human reviewers.
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Step 6: Open and review the pull request
Mastra positions the reviewed pull request as the output of the development workflow. The agent can take an issue through implementation, but the team still decides whether the final change should merge.
For early adoption, preserve that human merge gate. Automatic merging should be a later decision reserved for narrowly defined classes of low-risk work with excellent tests and rollback.
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Where Slack fits
Mastra's launch materials also mention Slack connectivity. That can help keep work visible to the team and bring agent activity into the collaboration layer, but Slack should not become a hidden approval channel for sensitive code changes. Keep authoritative state in the workflow and repository.
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Manual vs automatic stages
Factory lets teams choose manual or auto behavior by stage. A useful progression is:
- Intake: automatic
- Triage: agent-assisted with human answers when needed
- Planning: human approval
- Build: automatic in sandbox
- Review: automated checks plus human code review
- Done: automatic after merge/closure
This is an example operating pattern, not an official requirement. Teams should choose autonomy based on risk and verification quality.
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Existing codebases
Mastra says Factory can connect to an existing GitHub repository, inspect it, plan changes, implement work, run checks and open pull requests. That makes the product more relevant than a greenfield demo tool, but existing repositories vary enormously in documentation, architecture and test quality.
Before rolling it out widely, create a small benchmark set from your own backlog: a few bug fixes, refactors, tests and modest features. Measure review effort and failure modes, not just whether the agent creates a PR.
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What the vendor metrics do and do not prove
Mastra says Factory now writes around 25–35% of its pull requests and closes 50–60% of its issues internally. Those numbers show meaningful internal adoption, but they do not establish that another team will see the same rates. Repository structure, issue quality, tests, chosen models and review standards all change the result.
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Best use cases
Factory is strongest where work can be expressed as a bounded issue and verified through code review and automated checks: bug fixes, tests, contained features, refactoring and recurring maintenance.
It is weaker as a substitute for product judgment, vague architectural work or changes whose correctness cannot be meaningfully tested.
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Bottom line
The useful idea behind Mastra Factory is the lifecycle, not just the coding agent. GitHub and Linear provide the work source, agents handle structured stages, and humans retain explicit control over plans and merges. That makes Factory an interesting middle ground between manual developer workflows and fully autonomous software agents.
Sources
Primary and supporting sources
Facts were rechecked against the linked sources immediately before publication. Pricing, product availability and rollout status can change.