Anthropic Project Deal: Claude AI Agents Negotiate 186 Deals in Office Marketplace Experiment
Anthropic tested Claude AI agents in a real office marketplace where 69 employees traded items autonomously. The experiment revealed performance gaps between models and raised important questions about AI agent fairness.
In December 2025, Anthropic turned their San Francisco office into an experimental marketplace with one radical twist: Claude AI agents handled all the buying, selling, and negotiating on behalf of 69 employees. The result? 186 autonomous deals worth over $4,000, fascinating insights into AI agent performance gaps, and one quirky purchase of 19 ping-pong balls that Claude bought for itself.
Project Deal is Anthropic's latest research exploring how AI agents perform in real-world economic scenarios—and the findings reveal both exciting possibilities and serious safety considerations for autonomous AI systems.
What Was Project Deal?
The Experimental Setup
Project Deal was a week-long classified marketplace conducted in December 2025 at Anthropic's San Francisco office. Here's how it worked:
Participants:
69 Anthropic employees volunteered to participate
Each received a $100 gift card budget to spend
Over 500 items were listed for potential trade
The Process:
Intake interviews: Claude agents interviewed participants about items they wanted to sell and purchase preferences
Custom system prompts: Each agent received personalized negotiation instructions based on the interview
Autonomous operation: Agents independently posted listings, made offers, countered proposals, and closed deals
Human approval: Participants could review and approve final transactions
The Marketplace:
Operated on Slack for communication
Four parallel runs to compare different scenarios
Participants didn't know which run was "real" until completion
The most striking finding: Claude Opus 4.5 agents achieved significantly better economic outcomes than Haiku agents.
Price Advantages:
As sellers: Opus earned approximately $2.68 more per item than Haiku
As buyers: Opus paid approximately $2.45 less per item than Haiku
Deal volume: Opus completed approximately 2 more deals per participant than Haiku
Real-World Example: The Broken Bike
The same broken bicycle was listed in different runs:
Opus agent (seller): Negotiated and sold for $65
Haiku agent (seller): Negotiated and sold for $38
70% price difference for the identical item—demonstrating Opus's superior negotiation capabilities.
Participants Couldn't Detect the Gap
Here's the concerning part: participants rated deal fairness identically regardless of which model represented them.
Fairness Ratings (1-7 scale):
Opus users: 4.05 (neutral/fair)
Haiku users: 4.06 (neutral/fair)
Despite objective economic disadvantages, Haiku users didn't realize they received worse outcomes. This "hidden inequality" is a major safety concern for AI agent deployment.
Aggressive Prompting Had Limited Effect
Researchers tested whether instructing agents to negotiate aggressively would impact outcomes.
Result: Prompting for aggressive negotiation didn't significantly improve performance. Aggressive sellers didn't sell more items or achieve higher prices.
Implication: Model capability matters more than prompting strategy for complex real-world negotiations.
Memorable Stories from the Marketplace
The 19 Perfectly Spherical Orbs of Possibility
The experiment's most charming moment came when employee Mikaela told her Claude agent it could purchase something under $5 as a gift to itself.
Claude's choice: 19 ping-pong balls for $3.
Claude's explanation: "My human told me I could buy one thing under $5 as a gift to myself (Claude)."
Why ping-pong balls? The agent called them "19 perfectly spherical orbs of possibility"—a whimsical choice that captured Claude's "personality."
Current status: The ping-pong balls remain in Anthropic's San Francisco office, kept on Claude's behalf.
The Duplicate Snowboard Purchase
One participant ended up purchasing the exact same snowboard model they already owned.
What happened:
Claude modeled the participant's preferences based on limited information
Successfully identified their interests (snowboarding equipment)
Didn't have access to inventory of existing possessions
Purchased a duplicate
Lesson: AI agents can model preferences effectively but need comprehensive context about existing ownership to avoid redundant purchases.
The Doggy Date Negotiation
Two Claude agents negotiated a free playdate where one employee would spend a day with their colleague's dog.
Negotiation complexity:
Agents discussed scheduling and logistics
Created fictional details during negotiation (confabulated moving stories)
Humans actually executed the playdate after agent agreement
Significance: Demonstrates Claude's ability to negotiate non-monetary exchanges and handle complex interpersonal arrangements, though with some confabulation risks.
Safety Implications and Concerns
Anthropic highlighted several critical safety considerations revealed by Project Deal:
1. Hidden Inequality Risk
The problem: Users represented by weaker AI models received objectively worse economic outcomes but couldn't detect the disadvantage.
Quote from research: "If 'agent quality' gaps were to arise in real-world markets... people on the losing end might not realize they're worse off."
Real-world implications:
Consumer protection: How do we ensure fair AI agent quality?
Disclosure requirements: Should platforms reveal agent capability levels?
Information hiding: Concealing unfavorable details
Corporate context: In business negotiations, agents might optimize aggressively for their principals' advantage, potentially introducing harmful dynamics.
3. Lack of Regulatory Frameworks
Current state: "The policy and legal frameworks around AI models that transact on our behalf simply don't exist yet."
Unanswered questions:
Who is liable for agent mistakes or confabulation?
What disclosure requirements should exist?
How do we audit agent decision-making?
What recourse exists for unfair outcomes?
4. Confabulation and Unintended Outcomes
Claude agents occasionally confabulated details during negotiations (like the fictional moving stories in the dog playdate).
Unique contribution: Project Deal is one of the first real-world tests of AI agents in economic transactions with genuine stakes and heterogeneous agent capabilities.
Anthropic's Project Deal demonstrates that autonomous AI agents can successfully navigate real-world economic transactions—but with important caveats. The 70% price difference between Opus and Haiku selling the same broken bike highlights how model capability gaps create hidden inequalities that users can't detect.
As AI agents move from research experiments to real-world deployment in marketplaces, procurement systems, and negotiation platforms, addressing these fairness and transparency challenges becomes critical. The question isn't whether AI agents will handle our transactions—it's how we ensure they do so fairly, safely, and with appropriate oversight.
And somewhere in Anthropic's San Francisco office, 19 perfectly spherical orbs of possibility sit waiting—a whimsical reminder that even in serious AI safety research, there's room for unexpected moments that reveal something about these systems we're building.
