Engineering teams are being asked to deliver more—and faster—while quality expectations keep rising.
Too often, “more speed” means more rework: unclear requirements, inconsistent testing, and defects that surface late. The result is a familiar tradeoff between velocity and reliability.
Agentic and autonomous AI can change the equation. With role-based agents taking on repeatable software development life cycle (SDLC) work like drafting structured backlog assets, generating test cases and supporting autonomous testing, teams can increase capacity and throughput while strengthening quality. Humans stay in control, reviewing and refining outputs as agents continuously learn and improve over time.
Join this LinkedIn Live to see how agent-first delivery can extend team capacity, accelerate delivery and improve quality across the SDLC, from planning through release.
We’ll explore:
- Applying agentic AI across SDLC roles for faster, clearer handoffs with AI Assist™
- Reducing downstream churn by improving requirements and test quality early
- Scaling test coverage and consistency with autonomous testing patterns
- Putting lightweight governance in place so teams can adopt responsibly
- Learning practical lessons from Elevance
Walk away with concrete ways to start small, prove value and scale agentic AI across the SDLC, all focused on stronger outcomes and higher release confidence, not just faster output.
Vishveshwara Vasa
