Why 3 AI Agents Likely Beat 264 for Most Software Tasks

A source review of the Agency Agents GitHub repository — which hosts 264 specialized AI agent definitions and has amassed around 151,000 stars — argues that deploying large numbers of agents rarely improves outcomes. The reviewer found that the repository's real value lies in its MIT-licensed role library and selective installer, which allows users to pick a single role or a small subset rather than the full catalog. Key design principles from the project's own contribution guide stress that each agent must have a narrow specialization, concrete deliverables, and measurable success criteria, with near-duplicate roles explicitly rejected. The article recommends a minimal three-agent setup — a change engineer, a code reviewer, and a security auditor — each with restricted access and clear handoff rules, as sufficient for most software changes. The author cautions that role labels do not create real process isolation, and that agent count should be validated against actual correctness, cost, and latency benchmarks rather than assumed to scale with team size.