Imagine a code repository where the last human-made commit happened a month ago—and yet the project is buzzing: fixes are shipping, and new features work flawlessly. Is that just a futuristic vision, or an approaching reality that engineering leaders are already calling Agentic SDLC (an agent-based Software Development Life Cycle)?

In the face of the AI revolution, it’s worth asking a few key questions about the future of software delivery.

The traditional model—where a human is the “bottleneck” translating requirements into logic—is starting to give way to ASDLC (Agentic SDLC). In this new paradigm, AI agents (such as Devin or Claude Code) are no longer mere assistants, but autonomous units capable of planning, writing, testing, and deploying code based on natural-language instructions.

Instead of relying on the classic approach, we need new frameworks that emphasize not only what agents should do, but what they must never do.

This is one of the most important questions for business. In an AI-driven world, a vague requirement can be a project’s “death sentence”—because an AI agent will do exactly what it’s asked, even if it makes zero business sense.

So how does the analyst’s role change?

From documentation to prompt engineering: A core skill becomes the ability to express requirements precisely in language AI agents can reliably execute on.

A bridge between worlds: The analyst becomes a translator between the messy language of business and the structured language of machine instructions.

Managing non-determinism: AI excels where reasoning and decisions are needed based on unstructured data (e.g., customer intent analysis), shifting the focus from simple rules to modeling system behavior.

Research suggests that over a third of respondents (37%) fear AI will replace developers. However, many sources point to evolution rather than elimination. The developer becomes:

An Intent Engineer: Instead of writing loops, they validate whether the architecture proposed by AI is secure and scalable.

A Reviewer and Conductor: The role shifts toward an architect overseeing a “fleet” of AI agents.

Automation is already standard, but in Agentic SDLC testers move toward validating intent and ethics. Their job won’t just be checking whether a button works, but whether the system behaves in line with company values and legal/regulatory requirements (e.g., GDPR) in edge cases the AI didn’t anticipate.

Companies like Klarna already report that their AI assistants are doing work equivalent to 700 customer support agents—allowing them to freeze hiring for standard roles. Other studies suggest agentic approaches could shorten delivery time and reduce costs by as much as 56%.

Question for you: If tomorrow your team got an AI agent capable of writing any function in seconds—what would be the biggest challenge in your work? Would you know exactly what to ask for, and how to verify that the result is correct?

#AI #SDLC #BusinessAnalysis #SoftwareDevelopment #AgenticAI #FutureOfWork #ITTransformation