Comparing Multi-Agent Collaboration Strategies for AI-Assisted Software Testing

As modern continuous integration pipelines demand rapid software delivery, single-agent AI systems become bottlenecks in complex Software Quality Assurance (SQA) workflows. In partnership with Katalon, The Orchestrators developed a modular, reusable backend engine and benchmarking framework to orchestrate, evaluate, and optimize multi-agent collaboration in software testing.

The platform standardizes and empirically compares four distinct collaboration paradigms: structured SOP workflows (MetaGPT), hierarchical delegation (Manager-Worker), decentralized emergent swarms (AgentNet), and single-agent baselines. An integrated Evaluation Engine measures task success, output quality, collaboration efficiency, and token cost, while an innovative feedback loop leverages telemetry from past runs to dynamically refine prompts and agent behavior. By transforming opaque multi-agent interactions into verifiable, evidence-based metrics, the system empowers enterprise QA teams to deploy cost-effective, reliable AI agent workflows.

Key Highlights:

 • Multi-Strategy Benchmarking: Direct empirical comparison across MetaGPT, Manager-Worker, AgentNet, and single-agent baselines.

 • Evaluation-Guided Improvement: Automated closed-loop feedback mechanism to optimize agent prompts, coordination, and cost efficiency.