In “What if you’re building AI agents the wrong way?”, Tim Daines argues that many product teams falter with agentic AI not because of model limitations but due to flawed approaches rooted in traditional software thinking. He illustrates this with “Lizzie’s” project, which stalled when the team couldn’t define what the agent should know or decide, revealing that AI agents require cognition-first rather than UI-first design. Daines outlines five core skills for product managers: treating expert knowledge as structured infrastructure; using the Jobs to Be Done framework to architect memory in terms of core, recall, and archival functions; modeling cost from day zero (considering token usage, APIs, latency, etc.); embedding governance into design upfront (defining roles, data access, human hand-overs); and making explainability a requirement (via reasoning graphs, confidence scores, manual overrides) to build trust with stakeholders. He emphasizes that shipping agents is not just about prototypes or prompts, but about building systems with decision logic, cost discipline, policy, transparency, and memory. Ultimately, Daines contends that product management must evolve: agents demand thinking in systems, decisions, and trust. If these foundations are ignored, even promising AI agent projects may fail to scale beyond proof of concept.
This piece is something that caught my attention, so I thought I’d capture it as a tidbit. The OpenAI synopsis above is as of 9/22/2025 and may have an errant AI hallucination or two. Please support the original author(s) and visit their site for the whole story and accurate information:
https://www.mindtheproduct.com/what-if-youre-building-ai-agents-the-wrong-way/