A visual knowledge graph tracks the AI's understanding of the business, market, and customers. The CPO's goal is to increase this coverage percentage, which directly correlates with the ability to delegate higher-level strategic tasks to the AI agent.
Stakeholders must first interact with an AI agent about their feature ideas. The agent asks clarifying questions to assess impact and alignment. If the idea is weak, the agent politely rejects it, protecting PMs from distractions and poorly framed requests.
Delegating a company's core AI operating system to engineering or an 'AI ops' role is a mistake. The CPO, having the most 'skin in the game,' can iterate faster on feedback and ensure the agent's decision-making aligns with business strategy.
Counter-intuitively, summarizing transcripts before feeding them to an AI hurts retrieval accuracy. Summaries lose granular details and impose a distorting template. Storing raw text, which is cheap, provides a richer, more accurate knowledge base for the agent.
By analyzing transcripts of board meetings, an AI agent can abstract the mental models of board members. This creates a 'board skill' that can be used to 'poke holes' and get simulated feedback on new strategies or pitch decks before presenting them.
The company's AI agent monitors team communications. If it detects a disconnect—like a missing component or an attempt to hard-code a design—it automatically initiates a process to create and add the necessary component to the central Figma design system.
To find truly AI-native PMs, ask candidates what parts of their daily work they have automated. The spectrum of answers—from simply using a web UI to building a full agentic orchestrator—reveals their true level of craft and immersion in AI.
Using OpenClaw for general scaffolding and Hermes for its unique ability to automatically generate 'skills' for frequent tasks creates a powerful hybrid system. This led to a 31% improvement in recall metrics, as the agent became more accurate on recurring, specialized requests.
When an LLM agent fails a task, it often gives verbose, obvious advice on how to do it manually. This 'fake helpfulness' wastes tokens and time. The solution is to add a core imperative to the agent's instructions telling it to avoid this behavior.