DevSparks Hyderabad 2025: AWS' AI-DLC and Amazon Kiro compress months into hours

DevSparks Hyderabad 2025: AWS' AI-Driven Development Lifecycle resets how teams build software

Software engineering is changing faster than most teams can retool. Estimation falls apart, handoffs add drag, and the skill mix on teams is shifting. At DevSparks Hyderabad 2025, Raja SP, Head of Developer Acceleration at AWS, made a clear case for a new operating system for delivery: the AI-driven Development Lifecycle (AI-DLC).

From AI-managed to AI-native

Early patterns stalled. The "AI-managed" approach tossed vague prompts at a model and hoped for the best - fine for prototypes, not for production. The "AI-assisted" approach kept human-led orchestration with AI filling gaps - useful, but typically added only 10-15% productivity on enterprise pilots.

Raja's point: tools and plugins aren't enough. To see consistent 5x-20x gains, teams need a methodology that rethinks how work flows, how context is shared, and how decisions are audited.

AI-DLC in one line

Ask AI to plan; humans verify and refine; then let AI execute - with continuous tracking, reviews, and testable artifacts at every step.

Key practices you can adopt now

• Mob Elaboration: Product, engineering, and QA work together with AI from hour one. Turn ambiguous requirements into clear user stories, acceptance criteria, and delivery plans. What took months compresses into hours because context is collective, not siloed.
• Mob Construction: Before writing code, model the domain. Build component models, sequence diagrams, and functional flows. Then parallelize implementation with AI support. This reduces hallucinations, aligns contracts, and produces production-grade outcomes.

In this model, sprints shrink to days or hours. Work stops being rigidly sequential. Plans, reviews, and artifacts are always visible and auditable.

Amazon Kiro: specs in, production assets out

AI-DLC is embedded into Amazon Kiro, AWS's AI-powered IDE for spec-driven development. Kiro turns prompts into detailed specs, then into code, docs, and tests - while tracking every step: AI suggestions, human validations, and final decisions. That audit trail creates shared context across product, engineering, and QA without extra meetings.

A concrete result

Raja's team used AI-DLC with Kiro to build a learning app that assembles sequenced paths for subjects like PyTorch or Python, complete with resources, labs, and progress tracking. It was delivered in roughly two and a half hours, with solid design patterns and alignment to AWS Well-Architected principles. When teams see the story, the model, and the rationale tied to the code, trust follows.

Why this matters for enterprise delivery

• Tool-agnostic: AI-DLC is a methodology, not a single vendor play. Kiro helps, but the practices stand on their own.
• Audit-ready: Every plan and change is tracked, which supports compliance reviews and cross-team handovers.
• Scale-tested interest: Large organizations - including Wipro, S&P Global, Persistent Systems, and NASDAQ - are exploring the approach.

Run your first AI-DLC sprint

• Start with a clear problem statement. Ask AI to produce a delivery plan, architecture outline, and risk list.
• Review as a group (PM, Eng, QA). Tighten requirements, acceptance tests, and non-functional constraints.
• Model the domain: entities, contracts, and flows. Lock interfaces before coding.
• Generate specs, then code, tests, and docs. Keep humans in the loop at each gate.
• Track all decisions. Require test evidence before merge. Ship in hours, not weeks.

Get started

To try the approach, set up your team rituals around Mob Elaboration and Mob Construction, and standardize on audit trails for AI and human decisions. If you plan to use AWS services, create or link your AWS Builder ID for streamlined access to tools and docs.

If you're upskilling engineers for AI-native delivery, consider a structured path like this AI certification for coding to standardize skills across teams.

Bottom line

AI is no longer a sidecar. With AI-DLC, planning becomes explicit, modeling comes first, and execution is accelerated with traceability. The win isn't just speed - it's consistent, production-grade outcomes that teams can trust.