https://simple.ai/p/the-three-quotients-of-agent-success
The Dharmesh argues that in the “agentic” AI era, success is no longer just about IQ (intelligence) and EQ (emotional intelligence), but also about CQ: Context Quotient. For AI agents:
- IQ = how smart the model is.
- EQ = how well it interacts and collaborates.
- CQ = how much relevant, specific context the agent has about you—your business, goals, constraints, history, preferences.
The key idea is that agent success = IQ × EQ × CQ (multiplicative). If CQ is zero, the overall value is effectively zero, no matter how smart the underlying model is.
What CQ looks like in practice:
- For sales agents: knowledge of real closing behaviors, pricing exceptions and their reasoning, true loss reasons.
- For support agents: awareness of detractors’ and promoters’ feedback, known product issues and workarounds.
- For marketing agents: brand voice, audience segments, and performance history.
The article emphasizes that:
- You’d always choose a slightly less “smart” agent that knows your business deeply over a “genius” model that lacks context.
- Current industry focus on benchmark scores and raw model IQ misses the bigger differentiator: who has better context integration.
- High-CQ agents must:
- Capture the right context (including decision rationales).
- Maintain and learn from context over time.
- Connect context across disparate systems (CRM, analytics, support tools, etc.).
The author notes that this is exactly what they’re building at HubSpot: agents that deeply understand customer context across marketing, sales, and service, which gives unified platforms a structural advantage.
The article closes by suggesting that when you build or buy agents, you should ask:
- How much and what kind of context does the agent have?
- How does it learn about your business?
- Can it remember past decisions and improve?
- Does it connect to the systems where your context actually lives?
EQ’s role will be explored in a future piece, but for now, CQ is framed as the critical, often overlooked driver of real-world agent value.
Brilliant. We create our own LLMs so that they can speak in our voice, But what about our company's: Sales, Marketing, Customer Service, Finance, Product Research and Development, Manufacturing, Material Supply, "Fill in the Blank" all need Context.