Agentic Dependency Management

AI Software Composition Analysis (AI SCA) is software composition analysis built for AI-powered development. Unlike traditional SCA, which identifies open source risk after dependencies have been added to an application, AI SCA guides dependency decisions as developers and AI coding assistants write code. It combines trusted open source intelligence, policy enforcement, automated dependency management, and software transparency to help organizations securely scale development across the AI SDLC. Sonatype Guide is an AI SCA platform that applies these capabilities across the entire software development lifecycle, helping engineering teams build faster with less rework while giving security teams continuous control over open source risk.

AI coding assistants dramatically increase development speed, but they also increase the number of dependency decisions made every day. AI SCA helps developers choose trusted open source components, automate dependency management, reduce rework, and keep applications secure without slowing development.

AI code assistants are powerful accelerators, but without guardrails, they can unintentionally introduce serious risks into your software. These tools generate code based on patterns and predictions, not on verified security or quality standards.

Sonatype Research Labs recently revealed how AI models lack the real-time dependency, vulnerability, compatibility, and enterprise policy context required to make safe remediation decisions. Residual risk falls into three groups. About 60–70% can be removed through safe, non-breaking upgrades to Golden Versions. Roughly 20% require an engineering decision. The remaining ~10% reflects cases with no safe upgrade path.

The takeaway is clear: AI models are good at generating code, but they are not equipped to make dependency decisions on their own. Better software outcomes depend on combining model productivity with the real-time dependency intelligence, security policy, and enterprise context those models lack.

Sonatype Guide gives your AI coding assistants such as GitHub Copilot or Gemini Code Assist the context it needs to build code that requires little maintenance and minimal rework. It connects AI coding assistants to Sonatype’s industry-leading open source intelligence so that every component suggestion and autonomous upgrade is backed by accurate, real-time security and quality data.

Sonatype Guide is the AI SCA platform for modern development. It connects AI coding assistants, developer tools, and CI/CD pipelines to Sonatype's trusted open source intelligence, helping developers and AI agents choose secure, policy-aligned components from the start. Guide then automates dependency upgrades, governs open source risk, and maintains software transparency across the AI SDLC.

Sonatype Guide works with AI code assistants that implement the Model Context Protocol (MCP), including Gemini Code Assistant, Claude Code, Copilot, Windsurf, IntelliJ with Junie, Kiro, Cursor, and Codex (IDE Plugin & CLI). The Sonatype Guide MCP Server gives AI coding assistants access to trusted open source intelligence during development. Instead of relying on public package metadata or probabilistic recommendations, AI assistants receive verified guidance on secure component versions, vulnerabilities, malicious packages, license risk, and organizational policies. This helps developers and AI agents make better dependency decisions directly from their IDE.

Sonatype delivers the industry’s most comprehensive view of open source risk — combining full component data, secondary expansion data, and expert-curated vulnerability intelligence into a single, unified source of truth. Where others stop at basic CVE records, Sonatype goes deeper to map every component, dependency, and version to uncover hidden transitive risks and secondary impacts missed by traditional data feeds. Our intelligence blends proprietary research, automated machine learning, and human curation to ensure every data point is verified, contextualized, and actionable. The result: complete, precise, and early insight into vulnerabilities empowering developers and AI coding assistants to select the best components from the start.