Do you need a Co-Pilot or an AI Agent for your negotiations?

What's the difference between a co-pilot and an agent? Find out what you need for automated negotiations and why ensuring you have reliability is essential. 

People often ask whether Nibble could be used as a co-pilot for their teams. They want a tool which suggests negotiation responses or strategies while a human remains in control of the conversation. In 2023, Microsoft launched Co-pilot and led the way, positioning AI as a way to augment workers rather than replace them but more recently attention has turned to AI Agents which can execute task autonomously without human intervention.

The Secret About AI Co-Pilots

I probably shouldn’t say this (it might sound like I’m underselling Nibble), but if we built Nibble as a co-pilot, I doubt it would be any more effective than just good prompting in an LLM like ChatGPT or Claude. I don't see much use for specialised co-pilots which are expert in a specific niche (like negotiating) when the general purpose tools are so good.

Here’s why:

• LLMs Are Already Great Advisers Large language models (LLMs) have been trained on an incredible amount of information, including all the academic research on negotiation. They’re pretty good at it! In fact, studies show that simply chatting with an LLM before a negotiation to help you prepare can increase effectiveness by over 40% (credit to Yadvinder S. Rana).
  
  
• You’d Probably Ignore a Co-Pilot’s Advice Anyway If Nibble were a co-pilot, it would generate suggested responses for you which are very carefully crafted right down to the use of words and tone of voice but, let’s be honest, you’d likely tweak them to fit your own style and tone. Negotiation is personal. You would want to sound like yourself. And as soon as you start editing, you risk losing what makes Nibble effective in the first place.

The Power of an Agent: More Than Just the Words

Nibble isn’t just about the numbers and the bids. It’s almost more about the tone of voice, the behaviours, the timing—the way it pauses, the way it listens.

Negotiation is highly emotional. Think about asking for a payrise, buying a house, dare I say it but even negotiating for world peace has proved to be pretty emotional in the last week. The whole art of negotiation involves empathy, reading signals, and responding appropriately. Nibble is built to handle this, not just as a chatbot that spits out responses but as a negotiation specialist that truly engages.

Chris Voss is even trying something similar. His new AI mentor via MasterClass Oncall (in beta, it looks really interesting) lets you call in and get real-time negotiation advice (even on how to call in sick, apparently) from an AI version of Chris Voss trained on his course content and made to feel just like the real thing! But will chatting to AI-Chris be more effective than just asking ChatGPT? How would we even A/B test that?

The Reliability Factor: Why an Agent Works

The core reason Nibble is an agent and not a co-pilot is about trust. Unlike an LLM-based co-pilot, which could hallucinate or go off-script, Nibble is structured, reliable, and controlled in how it is built. We use LLMs inside the technology, but the responses it gives and the offers it makes are not generative.

✅ No hallucinations—it will never suggest deals outside your parameters

✅ No surprises—it follows strict negotiation behaviours, codified to deliver consistent results

✅ Insights at scale—it runs thousands of negotiations on your behalf and reports back with insights.

An Extra Team Member, Not a Tool

Think of your AI negotiation agent as an extra team member—one that can handle thousands of negotiations at once and report back not just on what happened, but why:

• What objections came up?
  
  
• What concessions mattered most?
  
  
• What can we learn about price sensitivity?

If you want a co-pilot, Nibble isn’t for you. But if you want an agent that negotiates on your behalf 100,000 times a month, this is it.

Find out more from Nibble's experience negotiating 100,000 times a month here. 

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