Automated software engineering
AI is making the unthinkable a reality: software that builds software. You can now dramatically accelerate your Software Development Lifecycle (SDLC), boosting productivity and quality. By adopting new workflows and development tools, you can transition to a near future where your applications are largely built and maintained automatically.
More in depth
Specification-driven development
- Move from “vibe coding” to structured, AI-ready specifications.
- Use context engineering to guide AI models to generate code that precisely meet requirements.
- Version specs and code together, and keep them independent from tools-specific formats, to avoid vendor lock-in.
AI across the development lifecycle
- Accelerate code reviews to detect risks, check spec alignment, and adherence to coding standards.
- Automated testing & QA, generating comprehensive test cases for faster and more reliable releases.
- Enforce security and performance integrating static and dynamic code analyses.
- Automate CI/CD pipelines implementing self-writing configurations to streamline delivery.
Legacy modernization
- Automate code migrations first, for language upgrades, platform porting, and refactoring existing codebases.
- Enable AI agents as a new class of users of enterprise applications.
- Refactor existing applications to expose or use capabilities with modern AI protocols like the Model Context Protocol (MCP) or Agent-2-Agent Protocol (A2A).
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