CodingAdvanced

Agentic engineering delivery loop

Use coding agents to move a scoped feature from ticket to tested change, while retaining review, security checks, and release accountability.

Setup time

3 hours

Time saved

5-15 hours

Best for

Engineering teams, Technical founders, Product engineers, Startups

Tools

Linear, Codex, Windsurf, Devin, GitHub Copilot

Quick answer

How does the “Agentic engineering delivery loop” workflow work?

This workflow treats coding agents as implementation partners, not autonomous release owners. The team scopes a change, keeps tests and review visible, and signs off before deployment. It takes about 3 hours, uses Linear, Codex, Windsurf, Devin, GitHub Copilot, and follows 5 documented steps.

Published by GPTNavi Editorial TeamLast materially updated 2026-08-19

Built from public product information and practical workflow-design patterns. Verify current pricing, features, and policies with each provider.

Overview

This workflow treats coding agents as implementation partners, not autonomous release owners. The team scopes a change, keeps tests and review visible, and signs off before deployment.

When to use this workflow

Feature delivery

Bug fix

Refactor

Test coverage improvement

Tools you need

Linear

Product management

Freemium

Issue tracking and product planning tool for engineering, product, and growth teams.

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Codex

AI coding

Freemium

OpenAI's agentic coding workspace for parallel implementation, code review, testing, and recurring engineering work.

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Windsurf

AI coding

Freemium

AI development environment with agentic coding, codebase context, and collaborative implementation workflows.

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Devin

AI coding

Paid

Autonomous software engineering agent for implementation tasks, codebase changes, tests, and pull-request workflows.

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GitHub Copilot

AI coding

Paid

AI coding assistant for code completion, explanations, and implementation support.

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Step-by-step workflow

Write an implementation-ready ticket

Define the user outcome, scope, acceptance criteria, non-goals, affected systems, risks, and test cases.

Tool used

Linear

Expected output

A bounded engineering ticket.

Ask for a plan before edits

Have an agent inspect the codebase and propose a file-level plan, assumptions, and tests; a human approves scope.

Tool used

Codex

Expected output

A reviewed implementation plan.

Implement in a contained branch

Use an AI development environment to make small, reviewable changes and keep the original acceptance criteria visible.

Tool used

Windsurf

Expected output

A focused change set.

Run a second-pass implementation

Use a second agent for clearly scoped tests, migrations, or repetitive changes, then compare its output against the plan.

Tool used

Devin

Expected output

Additional implementation and test evidence.

Review and release

Use AI assistance for review suggestions, but require a responsible engineer to validate tests, security, behavior, and release notes.

Tool used

GitHub Copilot

Expected output

A human-approved release candidate.

Prompt templates

Feature implementation plan

Inspect this codebase request and propose a minimal implementation plan. Include affected files, data changes, acceptance criteria mapping, tests, rollout risk, and rollback plan. Do not edit yet. Ticket: [paste]

PR review brief

Review this change against the ticket and tests. Find behavior regressions, security issues, edge cases, missing tests, migration risk, and scope creep. Give only actionable findings with evidence. Diff and ticket: [paste]