This is a guide showing how to plug ‘AI helpers’ into every step of your software development process so your developers have smart assistants that can plan, write, review, and maintain code alongside them.
Traditional software delivery relies heavily on manual effort across planning, coding, testing, and operations. This guidance explains how to systematically introduce agentic AI (AI agents that can take multi-step actions) into the SDLC to boost developer productivity, reduce errors, and shorten release cycles while staying within enterprise governance and security constraints.
For an enterprise implementing this, the defensibility comes from combining off‑the‑shelf AI agents with proprietary codebases, workflows, and SDLC data (repos, issues, CI logs). Over time, the organization’s unique prompts, integrations, and guardrails around these agents become a sticky, hard‑to‑replicate productivity system.
Frontier Wrapper (GPT-4)
Vector Search
Medium (Integration logic)
Context window cost and latency when agents orchestrate many tool calls across large codebases and SDLC artifacts.
Early Majority
This content focuses on integrating agentic AI specifically into the software development lifecycle, emphasizing practical rollout at scale, developer enablement, and alignment with existing GitHub/Copilot workflows—rather than being a generic AI-agents tutorial.