AI-native software development lifecycle (SDLC) is a TM Forum Innovation Hub project focused on accelerating ODA component engineering through trusted, AI-assisted software delivery. It demonstrates how agreed business requirements can be transformed into ODA-aligned designs, code, tests, deployment assets and evidence packs. The project brings together guided requirement clarification, AI-augmented engineering, maker-checker validation and human approval gates. It helps Members explore how AI can improve speed, quality and reuse across the software delivery lifecycle. The reference implementation validates selected ODA component scenarios on the ODA Canvas.
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Define and demonstrate a governed AI-native software delivery flow that takes an agreed ODA component scenario from requirement clarification through design, build, validation and deployment.
Produce practical assets Members can reuse, including agent skills, implementation patterns, component artefacts, tests, deployment guidance and evidence-pack templates.
Validate the reference implementation on the ODA Canvas and contribute learnings, controls and implementation evidence into the wider Agent Factory and Architecture-as-code blueprint.
Transforms agreed business requirements into ODA-aligned engineering artefacts, code, tests and deployment assets faster, helping members move from concept to working implementation with greater speed and consistency.
Applies maker-checker validation, human approval gates and conformance evidence to ensure AI-generated outputs are trustworthy, auditable and aligned with TM Forum standards.
Creates reusable agents, skills, patterns and deployment guidance that members can adopt, extend and contribute back into the wider AI-Native SDLC and Agent Factory ecosystem.
Feeds implementation learnings, controls and evidence into the Agent Factory architecture-as-code blueprint, helping shape a scalable industry approach for AI-native software delivery.
Ready to get involved and start building the future with industry innovators?