Factory vs AI Agent

Factory deploys Droids across your entire software development lifecycle, from triage and code generation through validation, release, monitoring, and documentation. Engineering teams at companies like Adyen, Groq, and Chainguard use it to ship production software with less manual toil. The platform connects desktop, CLI, chat, mobile, and issue trackers so agents can pick up work wherever your team already operates.

Where most coding agents stop at inline suggestions, Factory runs an autonomy stack across desktop, CLI, Slack, Teams, mobile, Linear, Jira, and GitHub. Mission Control dispatches parallel Droid workers on multi-week projects, and model routing lets you swap Claude, GPT, Gemini, or your own endpoint without changing workflows.

Factory is model-independent, routing across Claude, GPT, and custom endpoints for coding, reasoning, speed, and cost. Droids work from desktop, CLI, web, mobile, Slack, Jira, and Linear. Deploy as SaaS, hybrid cloud, on-prem, or fully air-gapped with your own LLM endpoint.

It fits engineering teams and platform groups that want autonomous agents handling routine SDLC work while humans set priorities, review output, and steer architecture.

AI Agent lets you build teams of autonomous agents that plan multi-step work, connect to live tools, and report back for approval. You assemble each agent with knowledge, skills, guardrails, and tool connections, then run them through chat, scheduled autopilots, or full workflows. Operations, growth, support, and engineering teams use it when they want repeatable playbooks instead of one-off prompts.

Where most agent builders stop at a chat box or a single Zap trigger, AI Agent pushes toward end-to-end runs with approve-before-send on outbound actions and optional multi-agent handoffs on the Max plan. Its Company Brain layer adds shared, read-only context across approved business tables so separate agents do not each start from a blank upload. That trade-off means more setup than a generic chatbot, but less babysitting than wiring one-off automations for every task.

Growth teams use it for funnel audits and churn monitoring tied to PostHog or Stripe. Support teams triage Gmail threads against policy docs and draft replies on a schedule. Product and engineering teams watch Linear and GitHub for stale PRs and open tickets. Marketers run SEO audits, competitor watches, and content briefs without writing integration scripts.