← Back to case studies
AI Engineering Initiative
Fully Agentic 360° Software Assessment Framework
Assessment time
hours of review
→
−70%
Checklist items
90+ per project
Human intervention
none
An independent AI engineering initiative focused on automating software project evaluation using multi-agent systems and agentic AI orchestration.
What changed
Before
Review
Manual
hours per project
Consistency
Varies by reviewer
After
Orchestrate
Multi-agent runner
Claude Agent SDK
Assess
Parallel agents
90+ checklist items
Tooling
MCP servers
LangChain
Output
Scored report
reproducible
A manual review checklist turned into parallel agents that each own a slice and write into one report.
What we built
The goal was to eliminate manual effort in software project assessments by building a fully autonomous, multi-agent AI system. The framework needed to evaluate architecture, code quality, security, DevOps, and monitoring across 90+ checklist items and produce professional HTML reports without human intervention.
Key Features
11 specialized Claude Code skills each autonomously assessing a distinct software dimension.
LangGraph stateful workflows managing multi-agent orchestration and parallel subagent dispatch.
Evidence-gathering via bash commands with adaptive scoring per checklist item.
MCP server integration for extended tool access during assessments.
Automated generation of HTML reports with radar charts and UML diagrams.
Claude Agent SDK used for skill composition and agent lifecycle management.
LangChain / LangGraph for stateful agent memory and workflow transitions.
Results
Fully automated assessment pipeline replacing hours of manual review.
90+ checklist items evaluated per project with consistent, reproducible scoring.
Multi-agent parallelism reduced total assessment time by over 70%.
Professional-grade reports generated end-to-end without human intervention.
Stack
Engagement: Ongoing · Product Engineering, AI Infrastructure & MLOps