Agentic Coding and AI SDLC

AI coding with delivery discipline.

We help engineering leaders control the review, test, security, and maintainability risks that appear when AI coding tools increase code volume.

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AI SDLC flow

Issue

Repo-specific agent instructions

Agent Draft

PR review policy

Review

Test and CI gates

Release

AI-generated code risk controls

Executive outcome

Clearer review ownership

Stronger regression discipline

Reduced unmanaged tool usage

Measurable adoption signals

AssessDesignDeployOperate

Focus

What we help establish

Repo-specific agent instructions

PR review policy

Test and CI gates

AI-generated code risk controls

Fixed-scope offer

AI Coding Agent Audit

For teams already using Cursor, Copilot, Claude Code, Codex, Gemini CLI, OpenHands, or similar tools, SNS offers a fixed-scope audit to assess ROI, review burden, test readiness, security risk, and rollout discipline.

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Common problems we solve

AI-generated pull requests are hard to review.

Developers use inconsistent prompts and workflows.

Test coverage does not keep up with generated code.

Secrets and sensitive areas are not protected.

Teams lack repo-specific instructions for agents.

Leaders cannot measure whether AI is helping.

Representative capabilities

AI coding-agent auditRepo-specific agent instructionsCode review and test gatesCI/CD enforcementSecret and dependency rulesAdoption measurement

Relevant tools and platforms

Examples only. Tool choice depends on the operating model, data boundary, security posture, and deployment target.

CursorGitHub CopilotClaude CodeCodexGemini CLIOpenHands

Next step

Discuss how S&S Data and AI Labs can shape this service for your organization.

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