Faster delivery. Clear architecture. Human accountability.
Our ADLC combines small work packages, agent-assisted execution and evidence-based review. People own the purpose, architecture and decisions; feedback from each release shapes the next iteration.
This is Last Requirement’s own framework. It is not an industry standard, a certification, or a universally agreed lifecycle.
Delivery that learns
Six recurring activities with human direction throughout. Select a stage to see what it produces and who is accountable for it.
Accountability
Define intent
Agree the problem, desired outcome and constraints before any build work starts.
One named person owns the intent and signs off the acceptance criteria with the client.
Acceptance criteria and measures of success.
A map, not a waterfall
The cycle describes recurring activities. Review can send work back to architecture or scope at any point, and small slices move through it repeatedly.
Independent tasks run in parallel within the agreed architecture, with integration and review before release. Parallel work is a property of stage 04, not a claim that everything happens at once.
Persistent responsibilities
These sit outside the numbered cycle. They apply at every stage rather than being reached in turn.
Two ways it shows up
How we deliver
Every engagement we run follows it, whether we are building the system or working inside your team.
What we help you adopt
Task specification patterns, review gates and working agreements your own developers can use.
Why it matters commercially
Agent-assisted work can produce more output than a team can examine. Three risks follow from that, and the review gates exist to catch them.
Architectural drift
Each change is defensible on its own while the system as a whole stops making sense. Recorded architecture decisions give reviewers something to compare against.
Unreviewable volume
Volume of output becomes volume of unexamined work. Small slices keep each change reviewable by a person who can say no.
Plausible but wrong
Output that reads well can still be incorrect. Deterministic tests, security checks, integration tests and human judgment stay in place; cross-model review is challenge, not proof.
Where the models run
We choose locally hosted or cloud models by task complexity, data sensitivity, deployment requirements and measured quality. Self-hosting alone does not establish privacy or security; the controls around it do.
Limits and escalation
Agent work carries iteration and cost limits. When an agent stops making progress, the task escalates to a person rather than looping. Traces, monitoring and cost visibility make that observable.
Considering this for your own delivery?
Tell us how your team works today and where agent-assisted work is causing friction.
Discuss your project