Dear Negotiation Explorer,

Welcome to Week 18 of our NegoAI series.

In Episode 3 with Prof. Remi Smolinski, we tackled a fundamental implementation question: when you deploy AI negotiation agents, what objective do you program them to pursue? The answer reveals more about organizational values than technical capabilities.

When companies implement AI negotiation assistants, they face a fundamental decision that shapes every interaction: should the agent extract maximum value from counterparts, or should it seek fair, Pareto-efficient outcomes that share benefits equally?

This choice isn't just technical—it's philosophical. Before you write a single prompt or configure any workflow, you need to define success explicitly. Your agent's objective function becomes the policy that drives every move at the table.

The role requesting the AI system drives different approaches. Sales representatives often prefer integrative negotiation because it moves them away from purely transactional discussions where they're forced to give discounts. When the conversation focuses on value creation rather than price competition, sales teams can defend margins more effectively. However, sales also requires a more nuanced approach—sometimes teams need agents that can handle very competitive buyers who focus primarily on price, requiring different tactics and positioning strategies.

Procurement presents equally complex choices, and this is where systematic routing becomes essential.

The most advanced implementations don't treat procurement as a single game. Instead, they use the Kraljic purchasing portfolio matrix to categorize suppliers and route to different negotiation playbooks automatically.

Here's how it works: your buyer agent first identifies which quadrant the supplier occupies, then selects the matching strategy. Strategic suppliers warrant partnership-focused approaches with joint roadmaps and innovation clauses. Leverage suppliers get competitive tension tactics with benchmarks and volume swaps. Bottleneck suppliers need supply security negotiations focused on dual-sourcing and service level agreements. Non-critical suppliers get efficiency-focused rate card discussions.

This creates a sophisticated challenge: developing a buyer agent that can classify suppliers using criteria like spend volume, supply risk, switching costs, and uniqueness, then communicate the appropriate strategy to the negotiation agent. The system must discern context before executing tactics.

We can also fine-tune the agent to shift strategic choice based on the behavior of the other party. During the preparation phase, it can build different scenarios. And when employed to assist during the negotiation, it can shift the strategic choice based on the other party's behavior.

Successful AI negotiation implementations rely on two key principles. First, continuous fine-tuning based on real-world performance. I fine-tune the system instructions so that they provide always more performant and reliable answers. Agents learn from each negotiation outcome, adjusting their approach based on what works and what doesn't in your specific business context. This iterative improvement process ensures agents become more effective over time while adapting to your organization's unique negotiation patterns.

Second, maintaining transparency through a "glass box" approach. Rather than deploying AI systems that make decisions without explanation, effective implementations make the assistant's logic visible and understandable. Users can see why the agent recommended specific tactics, how it weighted different factors, and what assumptions drove its analysis. This transparency builds trust and allows human negotiators to make informed decisions about when to follow AI recommendations and when to deviate based on situational factors the system might have missed.

I develop these workflows following a focused approach that starts with deep organizational research and workflow customization. I conduct thorough research on the company, their culture, and how people work, gathering all information I can about their negotiation patterns, decision-making processes, and business context. This allows me to customize the workflow specifically to the client's needs, adapting the agent behavior and logic to their unique situation.

Then I move to TypingMind for developing individual agents—this is where I build the core intelligence using external tools, web search capabilities, Model Context Protocol (MCP) integrations, sequential thinking processes, and memory MCP for learning from past interactions. TypingMind's flexibility with parameters and model selection makes it ideal for iterating on individual agent behavior and testing different approaches.

Once the individual agents perform well, I deploy them within integrated workflows in Cassidy. This is where four agents coordinate in sequence: first, a context expert agent (buyer expert for procurement scenarios, insight sales agent for sales contexts) analyzes the business situation and competitive landscape. Second, a behavioral agent assesses the counterpart's LinkedIn profile to understand their communication style and decision-making patterns. These two agents then inform the third agent—your negotiation strategist—which develops tactics, scenarios, and creative options based on both the business context and behavioral insights. Finally, a compiler agent synthesizes everything into a comprehensive, actionable report.

The workflow orchestration in Cassidy lets you test and fine-tune how these agents build on each other's outputs. The buyer expert and behavioral analysis create the foundation, the negotiation agent develops strategy using both inputs, and the compiler ensures nothing gets lost in translation.

The key advantage: you develop sophisticated individual capabilities in TypingMind's rich environment, then scale them through Cassidy's sequential workflow engine. This separation lets you version-control your agent instructions, compare performance across scenarios, and continuously improve both individual agent quality and multi-agent coordination.

Current AI negotiation agents and assistants outperform the average human negotiator in both preparation and execution. This advantage comes from systematic preparation, consistent application of negotiation principles, and emotional neutrality under pressure.

However, the best human negotiators still maintain advantages in reading complex social cues, adapting to unexpected situations, and building long-term relationships that extend beyond individual transactions. The gap is narrowing as we codify best practices and coach agents on organizational playbooks, but human expertise at the highest levels remains valuable.

The practical implication: standardize excellence by pushing your expert-level preparation into the assistant everyone uses daily. Well-trained agents already raise the floor across teams.

Being only a negotiation expert or only an AI expert isn't sufficient anymore. The future belongs to practitioners who combine negotiation expertise with AI literacy—teams that understand both human psychology and system capabilities.

This means teaching your people how to turn policies and playbooks into system instructions, how to feed structured context so agents reason with your facts and standards, and how to run tight human-in-the-loop processes where agents propose, negotiators decide, and outcomes feed back to improve the system.

Pick one negotiation context your organization handles regularly. How do they negotiate? Is it a price-first approach or a partnership-first approach or an integrative negotiation approach? Try to find ways to improve the negotiation process.