OpenQA
AI software engineering · Agentic SDLC

Agentic SDLC: AI across delivery, evidence at every step

Agentic SDLC (AI-SDLC) is a software delivery process in which AI agents carry requirements analysis, design, coding, testing and release. OpenQA is the verification part of it: every step leaves evidence, and a change that fails a quality gate goes back to coding with its root cause.

Agentic SDLC at a glance

  1. 1Requirements specRequirements analysisThe request is written up as a spec with features and acceptance criteria.Requirements analyst
  2. 2Technical design specDesign agentReads the existing code and produces the technical design spec.Architecture analyst
  3. 3ImplementationCoding agentImplements the change against both specs, writes unit tests, and runs the build and static scan before handing off.Software engineer
  4. 4Component testing and quality gateComponent test agentRuns unit tests with coverage, API tests and code review. If the gate fails, the defect analyst runs root cause analysis and it goes back to coding.Test engineerCode reviewerDefect analyst
  5. 5Test preparation (shift-left)ParallelTest design agent, test data agentTest cases and test data are prepared from the requirements and design.Test engineer
  6. 6Integration testing and quality gateIntegration test agent: API integration, end-to-endRuns integration tests with the prepared test cases and test data. If the gate fails, it goes back to coding with test evidence.Test engineerDefect analyst
  7. 7ReleaseRelease processOnce both quality gates pass, ships through the release process.

  • 01Spec-driven development

    Requirements and technical design are written as specs first; code and tests are baselined against them and stay traceable.

  • 02Two quality gates

    Component testing and integration testing each have a quality gate, and both must pass before release.

  • 03Closed-loop defects

    Defects that fail a gate return to the coding agent with root cause analysis and test evidence, and are regression-tested after the fix.

  • 04Shift-left testing

    Test design and test data preparation run from the specs while the code is written, off the critical path.

Illustrative flow. OpenQA Desktop is in internal use.

Each step: what AI does, and how OpenQA verifies it

Status is read from the capability table and matches the roadmap.

StepWhat AI doesOpenQA verification
RequirementsAn AI requirements analyst finds gaps and conflicts and writes a testable spec.
  • Requirement analysisAvailable
DesignAn AI architect designs against the existing code structure and marks affected modules.
  • Architecture wikiAvailable
  • Code knowledge graphInternal use · opens 2027 Q2
ImplementationAn AI engineer writes code and unit tests, then builds and runs static checks.
  • Evidence-based code reviewAvailable
  • Defect & security scanAvailable
Component testingAn AI tester selects tests by change impact, fills gaps and builds test data.
  • Change impact analysisAvailable
  • Test case generationAvailable
  • Test data constructionAvailable
Integration testingKey flows run in real browsers, APIs and devices; a pass needs visible evidence.
  • Browser executionAvailable
  • API automationInternal use · opens 2026 Q4
  • Cloud devicesInternal use · opens 2027 Q1
ReleaseThe quality gate returns merge, review or block; failures go back with the root cause.
  • OpenQA DesktopInternal use · opens 2026 Q4
  • Root-cause analysisAvailable

Shift-left testing: settle acceptance before code

AI writes code fast; if testing still waits until the end, defects pile up faster too. Shifting left means AI analyses the requirement, finds gaps and writes test cases before coding, so the coding agent knows what done means from the start.

CI/CD: bringing AI-written code into continuous integration and delivery

An AI-written change goes through CI like any other, but four more questions need answers: what it affects, whether what should be tested was, whether there are defects, and whether it can ship. OpenQA hands those answers and their evidence to the quality gate, which merges, sends for review or blocks.

Questions

Are AI-SDLC and agentic SDLC the same thing?

Largely, yes. AI-SDLC stresses AI entering the software lifecycle; agentic SDLC stresses agents that carry out each step on their own. OpenQA uses the latter and focuses on how each step is verified once an agent takes it over.

Do we have to replace our delivery process?

No. Keep your requirements tooling, repositories, CI and release process, hand steps to agents one at a time, and put quality gates at component and integration testing. Start with the verification of one real PR and widen it once it works.

How does AI-written code enter continuous integration and delivery?

Through CI like any other code, with extra questions answered: what the change affects, whether it was tested, whether there are defects, and whether it can ship. OpenQA Desktop (Internal use · opens 2026 Q4) returns a merge, review or block verdict with evidence for the gate; impact analysis, test generation and defect scanning are already available as open-source skills.

What does shift-left testing mean with AI?

When AI writes code quickly, testing cannot wait for the end. Shifting left means AI analyses the requirement, finds gaps and writes test cases before coding, so the coding agent knows the acceptance criteria from the start. The OpenQA Skills requirement and test case skills install straight into a coding agent.

Related terms