Factory

Why Choose Factory?

Factory is best understood as an agent-native engineering platform rather than a conventional coding assistant. Its main idea is that Droids should meet developers where work already happens: terminal sessions, IDEs, pull requests, Jira tickets, Linear issues, Slack threads, CI/CD jobs, and cloud or local development environments.

That positioning matters because many AI coding tools still assume the developer is sitting inside one editor and asking for help line by line. Factory is aimed at a more delegated workflow: describe the task, let the Droid inspect context, propose a plan, make changes, run checks, and hand back a reviewable result.

Core Workflow

A practical Factory workflow starts in the terminal or app. The developer opens a repository, starts Droid, asks it to analyze the codebase or complete a scoped task, reviews the plan, and approves or rejects modifications through the terminal UI or connected development surface.

For larger work, Missions provide a more structured approach. Instead of asking for one change, the user can split the task into stages: understand the codebase, design the implementation, make edits, run tests, review the diff, and summarize follow-up work. This is better suited to migrations, refactors, incident investigation, or cross-file changes.

The more important operational pattern is control. Factory’s adjustable autonomy levels and reviewable diffs are designed to prevent surprise changes. Teams should use that deliberately: allow routine edits to move quickly, but require approval for risky commands, broad file changes, secrets-related work, database migrations, or security-sensitive code.

Use Cases

Factory fits engineering workflows where the task has enough structure for an agent to make progress: bug fixes, refactors, migrations, dependency updates, local code review, security review, test generation, incident response, QA validation, documentation updates, and repetitive issue-to-PR work.

It is especially relevant for organizations that already have strong internal context in Jira, Linear, Slack, Notion, GitHub, CI/CD, and runbooks. A Droid becomes more useful when it can reference the same ticket context, engineering conventions, and repository knowledge that a human engineer would use.

Comparison to Alternatives

Compared with Devin, Factory feels more oriented around meeting developers inside existing tools rather than presenting one autonomous software engineer experience. Devin is a direct comparison for delegated engineering work, while Factory’s differentiation is its Droid workflow across CLI, IDE, team tools, cloud computers, automation, and enterprise governance.

Compared with OpenHands, Factory is less open-source and more commercial-platform oriented. OpenHands is attractive when transparency, local control, and self-hosting are the priority. Factory is more appealing when a team wants a packaged multi-surface experience, enterprise controls, managed remote computers, and integrated usage analytics.

Compared with Claude Code or Codex CLI, Factory is broader than a model-specific or terminal-only agent. Claude Code and Codex CLI are strong for individual terminal workflows, while Factory targets team-wide adoption with model selection, cloud/local agents, missions, Jira/Slack/Linear workflows, and admin policy.

Compared with Cline or Roo Code, Factory is not tied to a single VS Code extension experience. It can still work in IDE workflows, but its core value is the ability to follow engineering work across surfaces.

Best Configuration

For individual developers, start with the CLI in an actively maintained Git repository. Use small, reviewable tasks first: explain the architecture, review uncommitted changes, add tests around a function, or implement one ticket. This helps calibrate how Droid reasons about the codebase before trusting it with broader work.

For teams, AGENTS.md-style repository guidance, explicit test commands, naming conventions, architecture notes, and review rules are important. Agent quality improves when the codebase tells the agent how the team actually works. Connect Jira, Linear, Slack, or MCP only after the core repository workflow is reliable.

For enterprise rollout, define model policies and autonomy defaults before wide adoption. Expensive or experimental models should not be the default for every prompt. Use OTEL-based telemetry and usage dashboards to understand which teams, repositories, models, and workflows drive spend and productivity.

Migration Notes

Teams moving from autocomplete tools should not evaluate Factory only by suggestion quality. The better test is whether it can complete bounded work: investigate an issue, produce a safe diff, run checks, and explain tradeoffs. That measures the agent workflow rather than just code generation speed.

Teams moving from terminal agents should test persistence and cross-surface continuity. Factory’s value increases when sessions, missions, IDE usage, team context, and automation workflows connect instead of staying isolated in one terminal window.

Organizations comparing Factory with open-source agents should decide where they want control. If source availability and local-only operation are the top priorities, an open-source agent may be better. If governance, model selection, cloud computers, team workflows, and enterprise observability matter more, Factory is worth evaluating as a managed agent-native development platform.