Don't expect an AI agent to invent a successful sales process. First, have your human team identify and document what works—effective emails, scripts, and objection handling. Then, train the AI on this proven playbook to execute it flawlessly and at scale. The AI is a scaling tool, not a strategist from day one.

To fully leverage memory-persistent AI agents, treat the initial setup like an employee onboarding. Provide extensive context about your business goals, projects, skills, and even personal interests. This rich, upfront data load is the foundation for the AI's proactive and personalized assistance.

With AI agents, the key to great results is not about crafting complex prompts. Instead, it's about 'context engineering'—loading your agent with rich information via files like 'agents.md'. This allows simple commands like 'write a cold email' to yield highly customized and effective outputs.

Instead of explaining sales methodologies from scratch, the speaker copies the full transcripts from his company's internal training courses directly into the AI. The AI ingests this proprietary knowledge, creating a playbook it can apply to specific prospecting tasks like problem hypothesizing and messaging.

For any product involving ongoing user interaction (support, sales), the key differentiator is not raw model capability but a persistent knowledge base. This allows the AI to remember a user's history across sessions, transforming it from a simple question-answer tool into a stateful, effective partner that understands context.

Consistently feed your AI tool information about your company, products, and sales approach. Over time, it will learn this context and automatically tailor its sales prep output, connecting a prospect's likely problems directly to your specific solutions without needing to be reprompted each time.

Don't just use AI for one-way output. Close the loop by regularly feeding it data on what worked—booked meetings, positive replies, effective messaging. This creates a flywheel where the AI's intelligence layer gets progressively smarter, tightening processes and improving future prospecting results.

AI has no memory between tasks. Effective users create a comprehensive "context library" about their business. Before each task, they "onboard" the AI by feeding it this library, giving it years of business knowledge in seconds to produce superior, context-aware results instead of generic outputs.

Just as sales reps require training, AI agents need a consistent foundation of knowledge. This new concept of "agent enablement" involves feeding them curated data from calls, CRM, and playbooks to ensure their outputs are accurate and aligned with company strategy.