Our ADLC methodology delivers enterprise-grade software in 2-week production sprints — with mandatory Human-on-the-Loop governance so your team is always in control.

Production sprint cycle, not six-month waterfalls
Faster delivery on well-scoped enterprise builds
Human sign-off before any production deployment

ADLC ships in two-week agentic sprints. A traditional SDLC on the same scope runs three to six months of waterfall before anyone sees working software.
Robonito adaptive QA maintains regression coverage that heals itself as the UI changes. The traditional alternative is manual regression testing that falls behind the moment velocity increases.
Every pull request passes an AI security audit and a senior human review. Conventional delivery relies on human review alone, which is thorough on logic and inconsistent on security surface.
Agentic delivery only works in an enterprise if the controls are structural rather than optional. These four are enforced by the pipeline itself, not by policy documents.
No agent-generated code reaches production without a named senior engineer reviewing a diff, a risk score and a plain-language summary, then manually approving the deployment.
Enterprise data passes through a masking layer before reaching any model. Code agents never see raw customer data, and each runs in an isolated ephemeral container.
Every pull request is checked by an automated review agent and then by a human engineer against the approved architecture — not merely against whether the code runs.
Every gate produces a logged approval tied to a named engineer, available to you and your auditors — essential for regulated sectors across Saudi Arabia and the GCC.
Find answers to common questions about the ADLC methodology, governance, and how it fits your existing systems.
ADLC stands for Agentic Development Lifecycle — H&H Technology's proprietary software engineering methodology that uses AI agents to accelerate every stage of development while maintaining mandatory Human-on-the-Loop oversight.
No. Vibe coding and copilot-style AI assistance have no governance or structure. ADLC is a complete lifecycle framework: unit-test-first mandates, dual-layer PR review, self-healing QA, and a hard HOTL gate before any production deployment.
HOTL means no autonomous agent makes a consequential decision without a structured human approval gate. Before any code ships to production, a senior H&H engineer receives a diff, a risk score, and a plain-language summary — and manually approves the deployment.
All enterprise data passes through a PII masking and sandboxing layer before reaching any AI model. Code agents never see raw customer data, and every agent operates in an isolated, ephemeral container with no persistent network access.
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