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We Use AI Agents to Build Your AI Agents

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.

The problem we solve

Enterprise software that ships before the requirements move

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2 weeks

Production sprint cycle, not six-month waterfalls

3x

Faster delivery on well-scoped enterprise builds

100%

Human sign-off before any production deployment

The ADLC workflow

Four gates between an idea and production

Unit-test-first mandate
Agents write unit tests from your technical specification before a single line of code is generated. Quality is defined up front rather than inspected in afterwards.
01
Verification loop and self-healing QA
Generated code must pass every local test before it can be committed. Robonito then maintains AI-driven regression tests that adapt automatically as the interface changes.
02
Dual-layer pull request review
Every pull request is audited by an AI reviewer for security, then reviewed by a senior human engineer for alignment with your business logic. Two different kinds of mistake, two different catchers.
03
Human-on-the-Loop production gate
Nothing reaches production without a named engineer signing off against a diff, a risk score and a plain-language summary. Every decision is logged as a markdown record you and your auditors can read.
04
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Delivery speed

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.

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Quality assurance

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.

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Code review

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.

Governance and security built into the lifecycle

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.

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Human-on-the-Loop gate

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.

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PII masking and sandboxing

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.

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Dual-layer review

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.

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Full audit trail

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.

ADLC Frequently Asked Questions

Find answers to common questions about the ADLC methodology, governance, and how it fits your existing systems.

What does ADLC stand for?
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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.

Is ADLC just vibe coding or AI-assisted coding?
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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.

What is Human-on-the-Loop (HOTL) in the ADLC context?
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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.

How does ADLC handle enterprise data privacy?
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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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