What It Is
The agentic development lifecycle, or ADLC, adapts the traditional SDLC for a world where AI agents plan, build, test, and operate software alongside engineers. Agents handle well-defined tasks at each stage, while people set goals, review decisions, and approve changes.
Key Points
- Goal-driven work: engineers define outcomes and constraints; agents propose and carry out the steps.
- Continuous evaluation: automated evals and tests check agent output at every phase.
- Human checkpoints: approvals sit at design, merge, and release points.
- Feedback loops: production signals flow back into requirements and prompts.
Why It Matters
Where SDLC phases are often linear handoffs, the ADLC is iterative and continuous. It also treats agents themselves as products that need versioning, testing, monitoring, and governance. Without these controls, speed gains can bring hidden defects, security gaps, and runaway cost.
How ClearLeaff Applies It
Our AI × SDLC approach applies agentic AI across requirements, design, development, testing, deployment, and operations. We pair agents with human review, observability, and clear guardrails, so delivery gets faster without losing quality or control.