Build Something Agents Want

2026-05-13 · Source: The Business Engineer · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, quick

Summary

The traditional product management paradigm, centered on "build something people want" and assuming human evaluators, is undergoing a fundamental shift. This "topology" is breaking as artificial intelligence agents increasingly become the primary evaluators of products and tools. This change inverts the product management stack: wireframes evolve into schemas, user onboarding transforms into discoverability within a tool manifest, and retention is redefined as an agent's reuse of a tool. The core challenge is not merely building what agents want, but designing systems where local agent desires align with global human objectives, framing this as an incentive architecture problem rather than solely an AI problem.

Key takeaway

For AI Architects and Product Managers developing agent-facing tools, you must shift your focus from human-centric design to agent-centric evaluation. Prioritize designing incentive architectures that ensure agents' local preferences contribute to desired global human outcomes, rather than solely optimizing for agent performance. This requires rethinking traditional PM artifacts like wireframes and onboarding for agent interaction.

Key insights

Aligning agent desires with human goals requires an incentive architecture, not just AI development.

Principles

• Agent evaluation inverts PM stack.
• Retention becomes agent reuse.
• Local agent wants need global human alignment.

In practice

• Convert wireframes to schemas for agents.
• Prioritize tool manifest discoverability.
• Design for agent reuse metrics.

Topics

• AI Agents
• Product Management
• Incentive Architecture
• System Design
• Agent-Human Alignment

Best for: AI Architect, Entrepreneur, AI Product Manager, Director of AI/ML, Consultant

Related on AIssential

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• How to get multiple agents to play nice at scale — Coordinating multiple AI agents in complex systems is a significant engineering challenge.
• How People Actually Use AI Agents — Real-world AI agent autonomy is shaped by human interaction and trust, not solely by model capability.
• The Truth About Agents in Production — Effective AI agent development prioritizes type safety, robust evaluation, and a problem-first approach over complex multi-agent systems.
• Designing Effective Multi-Agent Architectures — Effective multi-agent system performance is an architectural outcome, not solely a prompting problem.

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Business Engineer.