You probably do not need 264 AI agents

Disclosure: Software Sausage is our product. Agency Agents did not sponsor, review, or endorse this article. AI tools helped draft and edit it; the evidence boundary is stated below. You probably do not need 264 AI agents. You need bounded roles and a gate The Agency Agents repository is difficult to ignore: 264 specialized agent definitions, broad coding-harness support, and—when I reviewed it on September 9, 2026—roughly 151,000 GitHub stars. The tempting conclusion is that a larger virtual team produces better work. The repository does not establish that. What it does provide is a useful, MIT-licensed role library and a competent installer. The value comes from selecting a few narrow roles and making their outputs pass real checks. This is a source review at commit 6d29a9b , not a benchmark. I inspected the role files, installer, converter, contribution rules, workflow example, and current GitHub checks. I did not evaluate every integration or the desktop app. What it gets right Agent definitions are not one-line personas. They name deliverables, workflows, constraints, and success metrics. The project converts them for Claude Code, Codex, Cursor, Gemini CLI, OpenCode, Qwen Code, Aider, and other harnesses. More importantly, the installer lets you select one role or division, show a dry run, and target an explicit path. Agency Agents itself warns that OpenCode currently registers only about 119 agents and recommends installing a subset. The project's contribution guide contains the best design rule: a new agent needs a narrow specialization, distinct behavior, concrete deliverables, measurable success, and real testing. Near-duplicate re-skins are rejected. That rule should apply to the workflow too. If two roles produce the same artifact, if nobody consumes an output, or if success cannot be checked, remove the role. What it does not prove The README's “never sleep” and “always deliver” language is marketing. Prompts still fail, time out, overrun context, and ag