What exactly is ADLC?

ADLC is a software development model in which AI agents carry out much of the delivery work, including planning, coding, testing and preparing releases. Engineers set the intent, define boundaries and review outcomes. The familiar stages of the SDLC remain. What changes is who executes them, which lets teams deliver faster without giving up human control.

Does ADLC mean building with agents or building agents?

The term is used both ways. In this article, ADLC means building software with AI agents, where agents handle much of the development work under human direction. Some companies, including EPAM , use ADLC for the lifecycle of building and operating AI agents as products. The two meanings overlap in one area: both require clear guardrails, continuous testing and human accountability.

What is the difference between SDLC and ADLC?

In a traditional SDLC, people execute each stage and tools assist them, so delivery speed depends largely on team size. In ADLC, AI agents execute much of the work in continuous loops, while engineers direct, verify and approve it. The main bottleneck moves from coding capacity to the clarity of requirements and the quality of human review.

Is ADLC the same as AI-DLC?

They describe very similar ideas. AI-DLC (AI-Driven Development Lifecycle) is the name AWS gave its own methodology, in which AI creates detailed work plans and defers critical decisions to humans. ADLC is a broader, vendor-neutral term for the same shift toward agent-led software delivery.

How much faster is software delivery with ADLC?

Results depend on how deeply a company redesigns its development process. McKinsey found that top-performing software organisations see 16 to 30% improvements in productivity and time to market, along with 31 to 45% gains in software quality. In a Dreamix internal agentic workflow , a feature estimated at 12.5 developer-days reached production in about 3 days, a 76% reduction in development time.

Does ADLC replace software engineers?

No. ADLC changes what engineers spend their time on. Agents take over much of the repetitive execution, while senior engineers set intent, design guardrails, verify agent output and own final decisions. That experience matters even more with agents, because AI output can contain errors and biases that only skilled reviewers catch before they reach production.

What are the biggest risks of adopting ADLC?

Uncontrolled costs, unclear business value and weak governance are the main risks. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027 for exactly these reasons. Companies reduce the risk by defining outcome metrics upfront, keeping human review gates in the workflow, and assigning experienced engineers to verify what agents produce.