AI can accelerate delivery without owning the decision.
Lupit uses AI and automation as parts of a controlled software-delivery system. They can help inspect context, propose changes, execute bounded work, and gather evidence. People remain responsible for scope, judgment, sensitive access, review, release, and the result delivered to users.
What is Lupit’s Agentic SDLC?
Lupit’s Agentic SDLC is a bounded delivery loop in which AI and automation can inspect context, propose or execute permitted work, and gather evidence. Human and policy-owned controls still govern scope, sensitive access, review, release, and accountability.
- Who is it for?
- It is for delivery contexts where AI-assisted work can be bounded by available context, permissions, verification, and an accountable owner.
- What is delivered?
- The loop produces reviewable changes and evidence tied to an objective, including tests, identified limitations, decisions, and an explicit release or escalation path.
- How does it work in an engagement?
- Authority is set before execution; tools act only inside that boundary; relevant checks are rerun; and a person or policy gate owns consequential decisions.
- What is not claimed?
- Agentic does not mean unsupervised autonomy, unrestricted production access, infallible output, or replacement of accountable product and engineering judgment.
What makes the workflow agentic
An agentic workflow can select and execute permitted steps toward a defined objective, use tools, inspect results, and continue or stop according to policy. That capability is useful only when the objective, available context, allowed actions, verification requirements, and escalation path are explicit.
The exact tools and level of automation depend on the project. This page does not claim that every delivery step is autonomous, that a particular model or provider is always used, or that AI output is accepted without review.
The bounded delivery loop
- 1
Define the objective and authority.
Record the requested outcome, acceptance criteria, repository or system boundary, permitted actions, and actions that require a person or project policy.
- 2
Load relevant context.
Use current task, code, documentation, dependency, and policy evidence rather than relying on a generic prompt or stale summary.
- 3
Execute a small increment.
Make one inspectable change, preserve traceability, and avoid expanding into unrelated work.
- 4
Verify independently.
Run deterministic checks, compare behavior with the acceptance criteria, and use a separate review context when the risk or project policy calls for it.
- 5
Release or escalate.
Proceed only through the approved gate. Record blockers, uncertainty, and evidence; do not silently bypass permissions, branch controls, or unavailable dependencies.
- 6
Carry learning forward.
Update task evidence, code, tests, and reusable project context so the next cycle starts from recorded facts.
Controls that remain human or policy-owned
product intent, prioritization, and acceptance of trade-offs
access to secrets, customer data, production systems, billing, and destructive actions
legal, privacy, regulatory, security, and publication decisions
exceptions to repository, review, branch-protection, and release policy
final responsibility for what is shipped and how incidents are handled.
Automation may prepare evidence or recommend a path, but it does not create authority that the project has not granted.
Evidence produced by the loop
A reviewable increment should leave the task and repository with enough evidence to reconstruct what happened: requirement-to-code traceability, focused test results, relevant lint/type/build checks, review findings, known limitations, and the branch, pull request, or release record. External CI and previews are useful clean-environment signals when available; their absence is documented and replaced with appropriate local or isolated validation rather than treated as proof of failure or success.