Magic

Magic is a software company that builds AI-native tools and infrastructure for software engineering teams, focused on integrating large language models into the software development lifecycle.

• AI-assisted software development platform for engineering teams (developer productivity tools).
• AI-native code generation and editing capabilities integrated into existing development workflows (software development tooling).
• Collaboration features that connect engineers with Artificial Intelligence (AI) systems to plan, write, and modify code (engineering collaboration).
• Infrastructure and orchestration for running AI agents against codebases and development environments (AI infrastructure for software engineering).
• Enterprise-focused deployment options and security posture for using AI in production engineering contexts (enterprise software development enablement).

More About Magic

Magic focuses on applying large language models and AI agents to software engineering workflows, with an emphasis on integrating AI into how teams design, write, and maintain code. Its offerings are positioned for engineering organizations that want AI systems to participate directly in source code changes, refactoring, and implementation tasks while preserving existing version control, review, and deployment processes. The platform targets scenarios where AI assists not only individual developers but also coordinated engineering teams working on shared repositories and services.

The company’s tools align with enterprise software development practices, including use of Git-based version control systems, pull requests, and Continuous Integration (CI) and delivery (CI/CD) pipelines (DevOps). Magic emphasizes AI-native development patterns, in which engineers interact with AI agents that can understand repository structure, coding standards, and project context. This model supports workflows such as generating new features from specification, updating code to match architectural changes, and making consistent edits across large codebases.

From a technology perspective, Magic builds on large language models (LLMs) (AI infrastructure) and integrates them with common developer tooling and protocols such as Git and standard code hosting platforms. The system typically operates against the live codebase, using repository indexing and context retrieval methods so AI agents can operate with awareness of files, dependencies, and interfaces. The platform is designed so that AI-suggested changes flow back as diffs or branches that humans can review, test, and merge under established governance practices.

In comparison to general-purpose coding assistants (developer productivity), Magic’s positioning centers on multi-step, agentic workflows that can span planning, implementation, and follow-up edits within an engineering project. This places it in marketplace categories such as AI-assisted software development, AI agents for engineering, and DevOps-adjacent tooling that connects AI with Continuous Integration and Continuous Deployment (CI/CD) and code review processes. The platform is oriented toward organizations seeking to embed AI into software delivery without replacing existing repositories, toolchains, or compliance controls.

For enterprise and institutional users, Magic’s value proposition focuses on integrating AI into engineering at the system level rather than only at the individual editor level. This includes support for collaboration between multiple engineers and AI agents on the same codebase, alignment with internal standards and security requirements, and deployment options suitable for production environments. In a directory or marketplace context, Magic fits within categories such as AI coding assistants, AI agents for software engineering, and AI-enabled DevOps tooling for development teams.