Personal AI engineering case study
Designing Diffwise as a multi-agent AI code review system
Diffwise is my personal project for incremental AI code review across 40+ specialist agents and nine review categories.
Updated September 2026
Quick answer
I designed Diffwise to review changed code with specialist agents instead of sending every file to one prompt. Incremental analysis reduces repeated work, while finding deduplication limits overlapping reports.
Evidence and decision points
40+ agents
Specialist agents inspect changed code through focused review responsibilities.
Nine categories
The review model separates concerns such as security, correctness, performance, and maintainability.
60% token savings
Incremental review avoids repeating unchanged context and reduced token use in project measurements.
Why one broad prompt was not enough
A broad review prompt can miss specialized concerns and repeat generic advice. It can also create duplicate findings when several checks identify the same underlying issue.
Diffwise separates review responsibilities and combines the results through a controlled finding pipeline.
Incremental review architecture
The system focuses analysis on changed code and necessary context. Trigger.dev coordinates long-running review work, Redis supports transient state, and PostgreSQL stores durable review data.
OpenRouter provides model access behind the review workflow. Finding similarity uses Jaccard comparison above 0.4 to identify likely duplicates.
- Route relevant changes to focused specialist agents.
- Keep durable review and finding state in PostgreSQL.
- Coordinate background tasks with explicit review status.
- Deduplicate overlapping findings before final presentation.
What the project demonstrates
Diffwise demonstrates agent workflow design, background execution, structured findings, model integration, and cost-aware incremental analysis.
It is a personal project. The figures describe project measurements and architecture, not a client result.
Sources and further reading
- OWASP Top 10 for LLM applications
OWASP documents risks that apply when models process code and use external tools.
- NIST AI Risk Management Framework
NIST provides a general structure for AI risk, measurement, and governance decisions.
Common questions
Is Diffwise client work?
No. Diffwise is my personal project and demonstrates my system design and implementation work.
Why use specialist agents?
Focused responsibilities make review criteria clearer and let the system route work by change type.
Does AI review replace human review?
No. It can find issues and organize evidence. A person still owns acceptance and engineering decisions.
Related resources
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LLM integration
I add language-model capabilities to existing products without forcing the complete product into one provider or one opaque workflow.
Guides
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Select an AI developer who can connect model behavior to a reliable product workflow and operating controls.
Discuss a defined project
Send the product context, current stack, expected outcome, constraints, and target timeline. I will reply with fit and next steps.
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