Devin

Devin is "the first autonomous software engineer," built by Cognition to help engineering teams work through their backlog. According to the company, "Devin plans, writes, tests, and ships production code on its own, working inside your codebase and the tools your team already uses." It's designed for engineering teams with complex, multi-repo projects — it learns a team's codebase and picks up "tribal knowledge" over time, and for large tasks Devin "can spin up a team of Devins," getting better by reading past session trajectories.

Cognition (the company behind Devin) lists customers including Mercedes-Benz, Goldman Sachs, Infosys, Anduril, Itaú, Cognizant, Nubank, and AthenaHealth.

Per Devin's docs, you sign up at app.devin.ai, then interact with Devin through several surfaces: the web app, a CLI (installed via curl -fsSL https://cli.devin.ai/install.sh | bash), or by tagging Devin in Slack or Teams. The /handoff command hands a task off to run on cloud Devin.

Beyond ad hoc requests, Devin can be:

• Assigned tickets directly in Linear, or picked up automatically via a "Devin" label
• Wired to GitHub to open and iterate on PRs, picking up review feedback and CI results until a PR is merged
• Connected to monitoring tools like Datadog, PagerDuty, and Sentry to automatically investigate alerts and errors
• Scheduled as recurring sessions/automations (for example, nightly error fixes or weekly dependency-update PRs)
• Driven through the Devin API and Devin Automations for fully automated workflows

Based on Cognition's own use-case gallery, organized by category with representative named examples:

• Incident response & issue triage — auto-investigating Datadog or PagerDuty alerts, auto-triaging Sentry errors, drafting structured incident postmortems, debugging a bug report end-to-end using logs and database access.
• Code migrations & modernization — migrating 50 files from REST to GraphQL, building a React 18 to 19 upgrade playbook, migrating a codebase from MongoDB to Postgres (converting schemas, rewriting queries, migrating data).
• Feature development — implementing an API from an OpenAPI/YAML spec, building a Stripe checkout flow, Dockerizing a Django app, converting a Figma design to pixel-perfect code.
• Recurring automations — nightly Sentry error fixes, auto-fixing failing CI builds, weekly dependency-update PRs, and compiling a weekly changelog from merged PRs.
• Data & reporting — ad hoc database queries typed in Slack (/dana), customer health summaries pulled via a playbook, custom API usage dashboards.
• Code quality & review — reviewing every PR for bugs and pushing fixes before merge, adding unit tests to an existing service, splitting a large monolithic router into domain-specific files.