Not all AI agents are created equal

The post argues that 'AI agent' covers very different systems, so teams should first categorize each idea by architecture before prioritizing. It proposes three categories (deterministic automation, reasoning and acting agents, and multi-agent networks), each with its own tooling, timelines, cost profile, and evaluation metrics.

01Key takeaways

• Comparing agent ideas by impact and effort fails when they differ architecturally; categorize them first.
• Most agent opportunities fit deterministic automation, which is the best place to start for quick, measurable ROI.
• Use reasoning agents only when the same request needs different action sequences depending on context.
• Multi-agent networks should rarely be a starting point; reserve them for cross-team coordination needs.
• Track completion rate, cost per run, and human review rate to tell whether each agent category is working.

02Frameworks mentioned

Summary and takeaways written by PM Atlas; quotes are short excerpts. © the original author.