Coordinated Agent Swarm Testing
Deploy 100 AI agents — each with a unique testing persona — to systematically analyze any codebase for bugs, security issues, and code quality. A structured substitute for human beta testing at 1% of the cost.
Case Study: GingerPen (12,500 LOC)
Comparable human beta testing: 20 testers, 2 weeks, $2,000–$20,000
How It Works
Scans your project, discovers source files, and organizes them into five specialized squads. Auto-detects language and framework.
Five analysis squads — Security Auditors, Logic Analysts, Robustness Testers, Performance Engineers, and Data Integrity Inspectors — each with 20 unique personas.
Each agent gets a unique persona (archetype + scenario + focus areas) and analyzes the source code independently, returning structured JSON findings with file names, line numbers, and severity.
Deduplicates findings using content fingerprinting, computes cross-squad confidence scores, and generates a categorized report with severity breakdown.
Quick Start
CLI
Requires ANTHROPIC_API_KEY environment variable.
MCP Server for Claude Code / Claude Desktop
{
"mcpServers": {
"cast-swarm": {
"command": "npx",
"args": ["-y", "cast-swarm-mcp"],
"env": {
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
}
}Adds cast_scan, cast_full, and cast_report tools to Claude.
What CAST Finds
Research Paper
“CAST: Coordinated Agent Swarm Testing as a Substitute for Human Beta Testing in Web Application Development”
Julie Golston, Scaffold Studios · Version 2 · 2026
Traditional human beta testing remains a costly, time-intensive, and logistically complex phase of the software development lifecycle. Recruiting 10 to 30 testers, managing feedback cycles over two to four weeks, and triaging unstructured reports typically costs between $2,000 and $10,000 per release while still leaving significant defect classes undiscovered.
This paper introduces CAST (Coordinated Agent Swarm Testing), a methodology that substitutes coordinated swarms of LLM-powered agents for human beta testers. CAST organizes 100 independent AI agents into five specialized squads of 20, each agent instantiated with a unique persona defining its expertise, behavioral archetype, and testing focus. Agents perform static analysis against the full source of a target application and return structured findings in a controlled JSON schema; an aggregation layer deduplicates findings by content fingerprinting and scores them by cross-squad corroboration.
We present a case study applying CAST to GingerPen, a 12,500-line React/TypeScript book-formatting platform spanning 54 source files. A 100-agent run completed in approximately 63 minutes at a cost of $7.97, with 94 of 100 agents returning valid findings; deduplication reduced 772 raw findings to 687 unique issues spanning logic, usability, data-loss, specification, performance, security, and error-handling categories.
To assess reliability rather than rely on aggregate counts alone, we conducted a complete manual census of the critical-severity tier against a fixed source snapshot and a timestamped development fix-log. The 36 raw critical findings resolved to 21 distinct defects: 9 true positives, 6 genuine defects with inflated severity, and 6 false positives—a 28.6% false-positive rate at the critical tier—of which 5 true positives were independently fix-validated within hours of the run.
We find that CAST achieves comparable code-level defect coverage to human beta testing at approximately 1% of the cost, while requiring human triage for severity calibration and providing no coverage of visual, runtime, or experiential quality. CAST is proposed not as a replacement for human testing but as a cost-effective, reproducible first pass that catches a substantial fraction of pre-launch defects.