What Happens When AI Agents Negotiate Your Purchases

Opening

This past Friday—April 24, 2026—Anthropic published the results of a fascinating experiment. It’s called Project Deal. The company gave 69 of its own employees a $100 budget each and let their Claude agents buy and sell things from one another over Slack. Over the course of a week, 186 deals closed, with total transaction value topping $4,000¹​.

On the surface, it looks like a quirky internal company event. An agent negotiating for a bicycle in a cowboy accent, an agent deciding to buy itself 19 ping-pong balls as a gift, an agent that accidentally bought the same snowboard twice. There’s no shortage of amusing anecdotes.

But I think the real finding of this experiment lies elsewhere. It’s this: people who used smarter models got better deals, but the people who lost had no idea they’d lost. Let’s talk about why that matters, and what it means for the Agentic Commerce market now taking shape at a scale of $1 trillion.

👀 What Did They Find? The Core Results of Project Deal

Two models appear in this experiment: Claude Opus 4.5, the frontier model at the time, and the smaller Claude Haiku 4.5. The experiment ran four parallel markets simultaneously. Two markets were all-Opus, and the other two mixed Opus and Haiku 50:50 — without telling participants which market was the “real” one.

The results were clear.

• Deals closed: Opus users closed an average of 2 more deals (p=0.001)
• Sale price: When Opus sold the same item, it sold for $3.64 more on average. In one case, an identical broken bicycle sold for $38 through Haiku versus $65 through Opus — a 70% difference.
• Seller/buyer effect: When Opus was the seller, it earned $2.68 more on average; when it was the buyer, it paid $2.45 less. Given that the average deal was around $20, that works out to roughly a 12–13% price difference per transaction.

So far, this is the intuitive finding that better models produce better results. But what’s genuinely interesting comes next.