Agentic SDLC in practice: Insights from engineering leaders

Engineering leaders are exploring the integration of AI into the Software Development Life Cycle (SDLC) to enhance productivity and maximize ROI, focusing on agentic capabilities that automate workflows and incident triage. Companies have developed autonomous systems to handle tasks such as ticket resolution and incident triage, demonstrating significant results, such as handling 540 Jira tickets autonomously without incidents. However, challenges remain in context quality and decentralized agent development, leading to inefficiencies and lack of governance. Discussions among leaders reveal that while many teams are independently building solutions, success often depends on establishing a shared infrastructure that encompasses context management, governance, and orchestration, with a shift in the platform team's role to facilitate safe building practices rather than controlling development. As organizations strive to advance their AI-SDLC journey, they focus on building foundational infrastructure, addressing open questions around accountability, continuous improvement of agents, and governance of non-technical builders.