{"id":2938,"date":"2026-04-25T06:03:29","date_gmt":"2026-04-25T06:03:29","guid":{"rendered":"https:\/\/aiopsschool.com\/blog\/?p=2938"},"modified":"2026-04-25T06:03:29","modified_gmt":"2026-04-25T06:03:29","slug":"human-to-ai-software-delivery-migration-with-claude-and-codex","status":"publish","type":"post","link":"https:\/\/aiopsschool.com\/blog\/human-to-ai-software-delivery-migration-with-claude-and-codex\/","title":{"rendered":"Human-to-AI Software Delivery Migration with Claude and Codex"},"content":{"rendered":"\n

Below is a step-by-step process to migrate your software delivery lifecycle from human-led<\/strong> to AI-assisted \/ AI-driven with human governance<\/strong>, using Claude + Codex<\/strong> across planning, architecture, HLD\/LLD, development, testing, and security scanning.<\/p>\n\n\n\n

Target model<\/h2>\n\n\n\n

Do not<\/strong> move directly from \u201chumans do everything\u201d to \u201cAI does everything.\u201d Use this maturity path:<\/p>\n\n\n\n

Stage<\/th>Ownership model<\/th><\/tr><\/thead>
Stage 1<\/td>Human does work, AI assists<\/td><\/tr>
Stage 2<\/td>AI drafts, human reviews<\/td><\/tr>
Stage 3<\/td>AI implements low-risk work, human approves<\/td><\/tr>
Stage 4<\/td>AI handles repeatable workflows, human audits<\/td><\/tr>
Stage 5<\/td>AI operates within strict SDLC guardrails<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

A good target is: AI prepares and executes; humans approve, monitor, and own accountability.<\/strong><\/p>\n\n\n\n

\n\n\n\n

Step-by-step migration process<\/h1>\n\n\n\n

Step 1: Inventory your existing system<\/h2>\n\n\n\n

Start by creating a complete map of your current application.<\/p>\n\n\n\n

For each microservice, document:<\/p>\n\n\n\n

Area<\/th>What to capture<\/th><\/tr><\/thead>
Service name<\/td>Purpose and business capability<\/td><\/tr>
Tech stack<\/td>Language, framework, database, queue, cache<\/td><\/tr>
APIs<\/td>REST, GraphQL, gRPC, events<\/td><\/tr>
Dependencies<\/td>Upstream and downstream services<\/td><\/tr>
Data ownership<\/td>Tables, schemas, topics, buckets<\/td><\/tr>
Deployment<\/td>CI\/CD, Docker, Kubernetes, cloud setup<\/td><\/tr>
Testing<\/td>Unit, integration, contract, regression<\/td><\/tr>
Security<\/td>Auth, roles, secrets, vulnerabilities<\/td><\/tr>
Operations<\/td>Logs, alerts, dashboards, runbooks<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

This becomes your AI context base<\/strong>.<\/p>\n\n\n\n

Without this step, Claude and Codex will produce guesses instead of reliable engineering output.<\/p>\n\n\n\n

\n\n\n\n

Step 2: Create an \u201cAI Context Pack\u201d<\/h2>\n\n\n\n

Create a standard folder in each repository, for example:<\/p>\n\n\n\n

\/ai-context\n  product-overview.md\n  architecture.md\n  service-boundaries.md\n  coding-standards.md\n  database-design.md\n  api-contracts.md\n  testing-strategy.md\n  security-rules.md\n  deployment-process.md\n  known-issues.md\n<\/code><\/pre>\n\n\n\n
Also add root-level instruction files such as:<\/p>\n\n\n\n
CLAUDE.md\nAGENTS.md\nCODEX.md\nCONTRIBUTING.md\n<\/code><\/pre>\n\n\n\n
These should explain:<\/p>\n\n\n\n
- What this service does\n- How to run it locally\n- How to run tests\n- Coding standards\n- Security rules\n- Branching strategy\n- PR expectations\n- What AI must never change without approval\n<\/code><\/pre>\n\n\n\n
This is the foundation for moving work from humans to AI.<\/p>\n\n\n\n
\n\n\n\n
Step 3: Define roles for Claude and Codex<\/h2>\n\n\n\n
Use both tools with different responsibilities.<\/p>\n\n\n\n
Area<\/th>Claude<\/th>Codex<\/th><\/tr><\/thead>
Planning<\/td>Strong<\/td>Medium<\/td><\/tr>
Requirement analysis<\/td>Strong<\/td>Medium<\/td><\/tr>
Architecture<\/td>Strong<\/td>Medium<\/td><\/tr>
HLD \/ LLD<\/td>Strong<\/td>Medium<\/td><\/tr>
Code generation<\/td>Medium<\/td>Strong<\/td><\/tr>
Refactoring<\/td>Medium<\/td>Strong<\/td><\/tr>
Test generation<\/td>Strong<\/td>Strong<\/td><\/tr>
Security review<\/td>Strong<\/td>Medium<\/td><\/tr>
Documentation<\/td>Strong<\/td>Medium<\/td><\/tr>
PR implementation<\/td>Medium<\/td>Strong<\/td><\/tr>
Code review<\/td>Strong<\/td>Strong<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Recommended split:<\/p>\n\n\n\n
Claude<\/strong>: planning, architecture, design, analysis, documentation, review, risk assessment.<\/p>\n\n\n\n
Codex<\/strong>: code changes, test creation, refactoring, bug fixing, implementation tasks.<\/p>\n\n\n\n
\n\n\n\n
Step 4: Convert your SDLC into AI-ready workflows<\/h2>\n\n\n\n
Your current flow may be:<\/p>\n\n\n\n
PLAN \u2192 Architecture \u2192 HLD \u2192 LLD \u2192 Development \u2192 Testing \u2192 Security Scan \u2192 Release\n<\/code><\/pre>\n\n\n\n
Convert it into this AI-assisted flow:<\/p>\n\n\n\n
Human request\n  \u2193\nClaude analyzes requirement\n  \u2193\nClaude creates plan + HLD + LLD\n  \u2193\nHuman approves design\n  \u2193\nCodex implements code\n  \u2193\nCodex\/Claude generates tests\n  \u2193\nCI\/CD runs tests and scans\n  \u2193\nClaude reviews PR and risk\n  \u2193\nHuman approves merge\n  \u2193\nDeployment automation releases\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Phase-by-phase migration<\/h1>\n\n\n\n
Phase 1: AI-assisted planning<\/h2>\n\n\n\n
Start with planning only.<\/p>\n\n\n\n
Give Claude business requirements and ask it to produce:<\/p>\n\n\n\n
- Functional summary\n- Non-functional requirements\n- Assumptions\n- Open questions\n- Risks\n- Affected microservices\n- Dependencies\n- Estimated implementation steps\n- Testing scope\n- Security impact\n<\/code><\/pre>\n\n\n\n
Human responsibility:<\/p>\n\n\n\n
- Validate assumptions\n- Confirm scope\n- Approve plan\n<\/code><\/pre>\n\n\n\n
At this stage, AI should not modify code.<\/p>\n\n\n\n
\n\n\n\n
Phase 2: AI-generated architecture and HLD<\/h2>\n\n\n\n
Next, allow Claude to generate architecture documents.<\/p>\n\n\n\n
For every feature, Claude should produce:<\/p>\n\n\n\n
1. Current system understanding\n2. Proposed architecture\n3. Affected services\n4. API changes\n5. Database changes\n6. Event\/message changes\n7. Security implications\n8. Scalability considerations\n9. Failure scenarios\n10. Rollback strategy\n<\/code><\/pre>\n\n\n\n
Human responsibility:<\/p>\n\n\n\n
- Architecture review\n- Security review\n- Approval before implementation\n<\/code><\/pre>\n\n\n\n
Recommended output:<\/p>\n\n\n\n
HLD.md\narchitecture-decision-record.md\nsequence-diagram.md\ndata-flow.md\nrisk-analysis.md\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Phase 3: AI-generated LLD<\/h2>\n\n\n\n
After HLD approval, move to LLD.<\/p>\n\n\n\n
Claude or Codex should generate:<\/p>\n\n\n\n
- Class\/module changes\n- API request\/response models\n- Database migration design\n- Validation rules\n- Error handling\n- Logging changes\n- Config changes\n- Unit test plan\n- Integration test plan\n- Rollback steps\n<\/code><\/pre>\n\n\n\n
Human responsibility:<\/p>\n\n\n\n
- Confirm detailed design\n- Confirm edge cases\n- Confirm backward compatibility\n<\/code><\/pre>\n\n\n\n
At this point, the AI should be able to create a very clear implementation plan.<\/p>\n\n\n\n
\n\n\n\n
Phase 4: AI-assisted development<\/h2>\n\n\n\n
Now introduce Codex for implementation.<\/p>\n\n\n\n
Start with low-risk tasks:<\/p>\n\n\n\n
- Unit tests\n- Small bug fixes\n- DTO\/model changes\n- API validation changes\n- Logging improvements\n- Documentation updates\n- Refactoring without behavior change\n<\/code><\/pre>\n\n\n\n
Do not start with:<\/p>\n\n\n\n
- Payment logic\n- Authentication\/authorization\n- Database migration-heavy changes\n- Security-sensitive code\n- Production incident fixes\n- Large cross-service redesigns\n<\/code><\/pre>\n\n\n\n
Recommended Codex workflow:<\/p>\n\n\n\n
1. Read requirement\n2. Read HLD\/LLD\n3. Inspect relevant service\n4. Create implementation plan\n5. Make code changes\n6. Add tests\n7. Run local checks\n8. Prepare PR summary\n<\/code><\/pre>\n\n\n\n
Human responsibility:<\/p>\n\n\n\n
- Review diff\n- Review tests\n- Approve PR\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Phase 5: AI-generated tests<\/h2>\n\n\n\n
Use AI heavily for testing.<\/p>\n\n\n\n
For each feature, generate:<\/p>\n\n\n\n
- Unit tests\n- Integration tests\n- Contract tests\n- API tests\n- Negative test cases\n- Boundary tests\n- Regression test cases\n- Security-focused tests\n<\/code><\/pre>\n\n\n\n
For microservices, prioritize:<\/p>\n\n\n\n
- Contract testing between services\n- Database migration tests\n- Message queue\/event tests\n- Backward compatibility tests\n- Idempotency tests\n- Retry\/failure tests\n<\/code><\/pre>\n\n\n\n
AI should also generate test matrices like:<\/p>\n\n\n\n
Scenario<\/th>Input<\/th>Expected result<\/th>Test type<\/th><\/tr><\/thead>
Valid request<\/td>Correct payload<\/td>Success<\/td>API<\/td><\/tr>
Missing field<\/td>Invalid payload<\/td>400 error<\/td>Negative<\/td><\/tr>
Unauthorized user<\/td>No token<\/td>401 error<\/td>Security<\/td><\/tr>
Duplicate request<\/td>Same request twice<\/td>Idempotent response<\/td>Integration<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\n\n\n\n
Phase 6: Security scanning and AI security review<\/h2>\n\n\n\n
Security should not be left only to Claude or Codex. Use AI plus automated tools.<\/p>\n\n\n\n
Your pipeline should include:<\/p>\n\n\n\n
- Static code analysis\n- Dependency vulnerability scanning\n- Secret scanning\n- Container image scanning\n- Infrastructure-as-code scanning\n- License scanning\n- API security testing\n- OWASP checks\n<\/code><\/pre>\n\n\n\n
Claude can review:<\/p>\n\n\n\n
- Authentication risks\n- Authorization gaps\n- Input validation\n- Injection risks\n- Data exposure\n- Logging of sensitive data\n- Secrets handling\n- Multi-tenant data leakage\n- Broken access control\n<\/code><\/pre>\n\n\n\n
Codex can fix:<\/p>\n\n\n\n
- Vulnerable dependency versions\n- Missing validation\n- Unsafe error messages\n- Weak test coverage\n- Simple security defects\n<\/code><\/pre>\n\n\n\n
Human responsibility:<\/p>\n\n\n\n
- Approve all security-sensitive changes\n- Review high\/critical vulnerabilities\n- Own final release decision\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Phase 7: AI-assisted code review<\/h2>\n\n\n\n
Before human review, let Claude review the PR.<\/p>\n\n\n\n
Ask Claude to check:<\/p>\n\n\n\n
- Does the code match the requirement?\n- Does it follow the HLD\/LLD?\n- Are there missing tests?\n- Are there security risks?\n- Are there performance issues?\n- Are there breaking API changes?\n- Are logs and errors appropriate?\n- Is rollback possible?\n<\/code><\/pre>\n\n\n\n
Then the human reviewer checks:<\/p>\n\n\n\n
- Business correctness\n- Architecture alignment\n- Security-sensitive areas\n- Production risk\n<\/code><\/pre>\n\n\n\n
This reduces human review effort but does not remove accountability.<\/p>\n\n\n\n
\n\n\n\n
Phase 8: CI\/CD integration<\/h2>\n\n\n\n
Your pipeline should become the enforcement layer.<\/p>\n\n\n\n
Minimum pipeline:<\/p>\n\n\n\n
1. Build\n2. Unit tests\n3. Integration tests\n4. Contract tests\n5. Linting\n6. Code quality scan\n7. Dependency scan\n8. Secret scan\n9. Container scan\n10. SAST\n11. DAST for deployed test environment\n12. Test coverage threshold\n13. Manual approval for production\n<\/code><\/pre>\n\n\n\n
AI can generate code, but CI\/CD should decide whether it is acceptable.<\/p>\n\n\n\n
\n\n\n\n
Phase 9: Start with one pilot microservice<\/h2>\n\n\n\n
Do not migrate all microservices at once.<\/p>\n\n\n\n
Choose one service that is:<\/p>\n\n\n\n
- Medium complexity\n- Well-tested\n- Not security-critical\n- Not payment-critical\n- Has clear ownership\n- Has stable APIs\n<\/code><\/pre>\n\n\n\n
Run a 4-week pilot.<\/p>\n\n\n\n
Measure:<\/p>\n\n\n\n
- Time to produce HLD\/LLD\n- Development cycle time\n- Number of AI-generated defects\n- Test coverage improvement\n- PR review time\n- Security scan results\n- Production incidents\n- Human effort saved\n<\/code><\/pre>\n\n\n\n
Only after success, expand to more services.<\/p>\n\n\n\n
\n\n\n\n
Phase 10: Define approval gates<\/h2>\n\n\n\n
Use this rule:<\/p>\n\n\n\n
Activity<\/th>AI can draft<\/th>AI can execute<\/th>Human approval required<\/th><\/tr><\/thead>
Requirement analysis<\/td>Yes<\/td>Yes<\/td>Yes<\/td><\/tr>
HLD<\/td>Yes<\/td>No<\/td>Yes<\/td><\/tr>
LLD<\/td>Yes<\/td>No<\/td>Yes<\/td><\/tr>
Code changes<\/td>Yes<\/td>Yes<\/td>Yes<\/td><\/tr>
Unit tests<\/td>Yes<\/td>Yes<\/td>Optional<\/td><\/tr>
Security fixes<\/td>Yes<\/td>Limited<\/td>Yes<\/td><\/tr>
Database migrations<\/td>Yes<\/td>Limited<\/td>Always<\/td><\/tr>
Production deploy<\/td>No<\/td>No\/limited<\/td>Always<\/td><\/tr>
Incident resolution<\/td>Assist only<\/td>Limited<\/td>Always<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\n\n\n\n
Recommended operating model<\/h1>\n\n\n\n
Human responsibilities<\/h2>\n\n\n\n
Humans should continue to own:<\/p>\n\n\n\n
- Product decisions\n- Architecture approval\n- Security approval\n- Production release approval\n- Incident accountability\n- Compliance decisions\n- Final code ownership\n<\/code><\/pre>\n\n\n\n
AI responsibilities<\/h2>\n\n\n\n
AI can increasingly handle:<\/p>\n\n\n\n
- Drafting plans\n- Writing HLD\/LLD\n- Generating code\n- Generating tests\n- Updating documentation\n- Performing first-pass review\n- Explaining code\n- Refactoring\n- Finding gaps\n- Preparing release notes\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Standard AI workflow for every new feature<\/h1>\n\n\n\n
Use this repeatable process:<\/p>\n\n\n\n
1. Product requirement is created.\n2. Claude analyzes requirement.\n3. Claude identifies affected microservices.\n4. Claude creates implementation plan.\n5. Claude creates HLD.\n6. Human approves HLD.\n7. Claude creates LLD.\n8. Human approves LLD.\n9. Codex creates code changes.\n10. Codex creates tests.\n11. CI\/CD runs checks.\n12. Claude reviews PR.\n13. Human reviews PR.\n14. Security scans must pass.\n15. Human approves release.\n16. Deployment happens through existing pipeline.\n17. Claude creates release notes and documentation.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Example folder structure for AI-driven SDLC<\/h1>\n\n\n\n
\/docs\n  \/architecture\n    system-overview.md\n    microservice-map.md\n    data-flow.md\n  \/adr\n    ADR-001-service-communication.md\n  \/features\n    FEATURE-123\n      requirement.md\n      plan.md\n      hld.md\n      lld.md\n      test-plan.md\n      security-review.md\n      release-notes.md\n\n\/ai-context\n  service-summary.md\n  coding-rules.md\n  test-rules.md\n  security-rules.md\n  deployment-rules.md\n<\/code><\/pre>\n\n\n\n
This makes every feature traceable from requirement to release.<\/p>\n\n\n\n
\n\n\n\n
Migration roadmap<\/h1>\n\n\n\n
First 30 days<\/h2>\n\n\n\n
- Document existing architecture\n- Create AI context packs\n- Select one pilot microservice\n- Use Claude for planning and HLD\n- Use Codex only for tests and small fixes\n- Keep humans fully in control\n<\/code><\/pre>\n\n\n\n
Days 31\u201360<\/h2>\n\n\n\n
- Use AI for LLD generation\n- Use Codex for feature implementation\n- Add AI-generated test coverage\n- Add Claude PR review\n- Strengthen CI\/CD quality gates\n<\/code><\/pre>\n\n\n\n
Days 61\u201390<\/h2>\n\n\n\n
- Expand to 3\u20135 microservices\n- Standardize prompts and templates\n- Introduce AI-generated release notes\n- Use AI for security review support\n- Measure productivity and defect trends\n<\/code><\/pre>\n\n\n\n
After 90 days<\/h2>\n\n\n\n
- AI handles repeatable engineering tasks\n- Humans focus on architecture, review, security, and product direction\n- Mature services can move to AI-first implementation with human approval\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Key risks and controls<\/h1>\n\n\n\n
Risk<\/th>Control<\/th><\/tr><\/thead>
AI misunderstands system<\/td>Maintain strong AI context docs<\/td><\/tr>
AI creates insecure code<\/td>Security scans + human security review<\/td><\/tr>
AI breaks contracts<\/td>Contract tests required<\/td><\/tr>
AI changes too much<\/td>Small PR size limit<\/td><\/tr>
AI exposes secrets<\/td>Secret scanning + no secrets in prompts<\/td><\/tr>
AI produces untested code<\/td>Test coverage gate<\/td><\/tr>
Humans overtrust AI<\/td>Mandatory review checklist<\/td><\/tr>
Architecture drift<\/td>HLD\/LLD approval gate<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\n\n\n\n
Practical starting point<\/h1>\n\n\n\n
Start with this sequence:<\/p>\n\n\n\n
1. Pick one non-critical microservice.\n2. Create an AI context folder for it.\n3. Ask Claude to generate the service overview.\n4. Ask Claude to generate missing HLD\/LLD from existing code.\n5. Human architect reviews and corrects it.\n6. Ask Codex to add missing unit tests.\n7. Run CI\/CD and security scans.\n8. Ask Codex to implement one small enhancement.\n9. Ask Claude to review the PR.\n10. Human approves and merges.\n<\/code><\/pre>\n\n\n\n
Once this works reliably, repeat across other services.<\/p>\n\n\n\n
\n\n\n\n
Final recommendation<\/h1>\n\n\n\n
Treat Claude and Codex as AI engineering team members<\/strong>, not as full replacements for your human team.<\/p>\n\n\n\n
A strong model is:<\/p>\n\n\n\n
Claude = Analyst + Architect + Reviewer\nCodex = Developer + Test Engineer + Refactoring Assistant\nHuman = Owner + Approver + Security\/Governance Authority\nCI\/CD = Final quality enforcement layer\n<\/code><\/pre>\n\n\n\n
The safest migration path is:<\/p>\n\n\n\n
Documentation \u2192 Planning \u2192 HLD\/LLD \u2192 Tests \u2192 Small Code Changes \u2192 Larger Features \u2192 AI-first Delivery with Human Approval\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"
Below is a step-by-step process to migrate your software delivery lifecycle from human-led to AI-assisted \/ AI-driven with human governance, […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2938","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/2938","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=2938"}],"version-history":[{"count":1,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/2938\/revisions"}],"predecessor-version":[{"id":2939,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/2938\/revisions\/2939"}],"wp:attachment":[{"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=2938"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=2938"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=2938"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}