Devin 2.2: Desktop and Code Review AI Guide

What Is New in Devin 2.2

Devin 2.2 represents the largest feature update since Cognition AI first launched the autonomous AI engineer in early 2024. The December 2025 release (Devin 2.0) focused on reliability and expanded language support. This February 2026 update shifts the product into new territory with GUI interaction capabilities and automated code review.

Additional improvements include better context handling for large codebases, $10 in free monthly credits for all users, and refinements to the planning and execution pipeline that reduce hallucinated file paths and incorrect import statements. These changes position Devin as a more practical tool for daily engineering workflows rather than a demonstration-only product.

Desktop Computer-Use Support

The most significant addition in Devin 2.2 is desktop computer-use support. Previous versions of Devin operated exclusively through terminal commands, code editors, and browser-based development environments. The new computer-use capability allows Devin to interact with any GUI desktop application by seeing the screen, moving the cursor, clicking elements, and typing into fields.

What Desktop Computer-Use Enables

• Design-to-code workflows. Extract design tokens, measurements, and assets directly from Figma or Sketch files without manual export steps.
• Browser-based testing. Navigate web applications, fill forms, click through user flows, and verify visual rendering in real browsers.
• Cross-application data transfer. Copy data between applications that lack API integrations, such as moving spreadsheet data into a CMS or extracting information from PDF viewers.
• Image and media manipulation. Perform basic Photoshop operations, batch resize images, or apply consistent filters across a set of assets.

This feature puts Devin into a category that terminal-only tools like Claude Code and Cursor cannot currently match. While those tools excel at code generation and refactoring within editors and terminals, they have no mechanism to interact with visual applications. For teams whose workflows span design tools, browsers, and code editors, desktop computer-use eliminates several manual handoff points. The WordPress AI assistant integrations represent another approach to bridging the gap between visual editors and AI automation.

Devin Review: Self-Reviewing PRs

Code review is the bottleneck in most AI-assisted development workflows. An AI agent can generate a pull request in minutes, but a human engineer still needs to review it carefully, often spending more time reviewing AI-generated code than they would have spent writing it themselves. Devin Review addresses this by adding an automated review layer between code generation and human review.

Cognition AI reports that Devin Review catches approximately 30% more issues compared to PRs submitted without the review step. In practice, this means fewer review cycles between AI and human engineers, faster merge times, and less cognitive load on the reviewers who are already dealing with high PR volumes.

The review system is particularly effective at catching issues that are easy to miss in large diffs: unused variables, inconsistent error handling patterns, missing null checks, and test coverage gaps. It does not replace human review for architectural decisions or business logic validation, but it handles the mechanical checks that consume disproportionate review time.

Performance and Speed Improvements

One of the most persistent complaints about Devin has been session startup latency. In Devin 2.0, initializing a new session took approximately 45 seconds as the system loaded the codebase context, set up the development environment, and established tool connections. Devin 2.2 reduces this to approximately 15 seconds, a 3x improvement that fundamentally changes how the tool fits into daily workflows.

Beyond startup speed, Devin 2.2 includes improvements to context handling for large codebases. The system now indexes repository structure more efficiently, reducing the number of hallucinated file paths and incorrect import statements that plagued earlier versions when working with repositories exceeding 100,000 lines of code. These reliability improvements compound with the speed gains to make Devin meaningfully more useful for production engineering work.

Linear Integration and Workflow

Devin 2.2 introduces native integration with Linear, the project management platform widely used by engineering teams. The integration allows Devin to pick up assigned tickets directly from Linear, work on them autonomously, and update ticket status as it progresses through the implementation.

Integration Workflow

• Ticket assignment. Assign a Linear ticket to Devin just as you would assign it to a human engineer. Devin reads the ticket description, acceptance criteria, and linked issues.
• Autonomous execution. Devin plans the implementation, writes the code, runs tests, and applies the Devin Review self-check before creating a pull request.
• Status updates. Linear ticket status updates automatically as Devin moves from "In Progress" to "In Review" to "Done," keeping project boards accurate without manual intervention.

The Linear integration is particularly valuable for teams that triage a backlog of small-to-medium tasks, such as bug fixes, dependency updates, test additions, and documentation improvements. Instead of assigning these to engineers who would rather work on complex features, teams can route them to Devin and review the resulting PRs. This pattern works best when tickets have clear acceptance criteria and well-defined scope.

Devin vs Claude Code vs Cursor

The AI coding tool landscape in February 2026 has three dominant approaches, each optimized for different workflows. Understanding their strengths helps teams allocate the right tool to the right task rather than forcing one tool to do everything.

The practical recommendation for most teams is to use Cursor for fast inline edits and pair-programming style work, Claude Code for terminal-heavy operations and large-scale refactoring, and Devin for tasks that require autonomy, background execution, or GUI interaction. The tools are complementary rather than mutually exclusive, and the teams getting the most value from AI coding assistance are using two or three of them.

Pricing and Free Credits

Devin 2.2 introduces $10 in free monthly credits for all registered users, lowering the evaluation barrier significantly. Previous versions required a paid plan to access any functionality. The free credits provide enough compute for several meaningful coding sessions, allowing teams to test Devin on real project work before committing to a subscription.

For comparison, Claude Code charges per API token used (with costs varying based on the Claude Opus 4.6 pricing), Cursor offers a free plan with limited completions plus Pro at $20 per month, and GitHub Copilot runs $10-39 per month depending on the tier. Devin is the most expensive per-seat option, but its autonomous execution model means it can work unsupervised while engineers focus on higher-complexity tasks, potentially justifying the premium for teams with large backlogs.

Getting Started with Devin 2.2

Setting up Devin 2.2 requires connecting your code repository, configuring access permissions, and defining the types of tasks you want Devin to handle. The process takes approximately 15-30 minutes for a standard GitHub or GitLab repository.

The most common mistake when adopting Devin is assigning tasks that are too broad or ambiguous. Tasks like "refactor the authentication module" produce inconsistent results because Devin needs to make subjective decisions about scope, approach, and tradeoffs. Tasks like "add input validation to the /api/users endpoint following the pattern in /api/products" produce reliable results because the scope, pattern, and location are all specified. Start narrow and expand as you learn how Devin interprets your project conventions.