How to use agentic AI in your SDLC

Agentic AI in the Software Development Lifecycle (SDLC) involves using AI agents that autonomously plan and execute engineering tasks across the development process, such as writing and testing code, running tests, and preparing changes for release. Unlike traditional AI coding tools that merely suggest code completions, agentic AI requires infrastructure support as it actively executes commands and iterates tasks until completion. Implementing agentic AI involves six steps: defining clear tasks, using isolated sandboxes, integrating changes through Git and pull requests, verifying in preview environments, ensuring controlled release workflows, and applying governance controls. Northflank supports these steps by offering microVM-backed sandboxes, Git-based builds, preview environments, and robust governance features, allowing teams to maintain quality while integrating agentic AI into their existing processes.

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