How to Build Your Dynamic Pricing & Overbooking Agent

• Microsoft Copilot Studio
• AWS Bedrock
• Google Gemini Enterprise
• Salesforce Agentforce
• Cursor — the walkthrough below uses Cursor; Claude Desktop works the same way
• More platforms being added as they gain adoption

You're not locked in — the same underlying intelligence connects to whichever builder your organization runs on.

Step 2: Add a Data Source

Every agent builder needs to know where your bookings live. There are two ways to connect them:

Step 3: Choose MCP Where Possible

CSV upload works, but MCP is the preferable path — it's the secure, standardized way for an agent to access a calculated price and booking limit, rather than a static file that goes stale the moment you export it.

Steps to Enable MCP on AI Platforms

Say you send a booking export for next weekend. The agent needs your capacity, your past bookings and the prices you charged. From that it recommends a price, how many rooms, seats or slots to hold back, and how many extra bookings to take, with the reasoning shown.

1. You get a recommended price for right now. optimize_price uses remaining capacity, days to the deadline and how demand has behaved before. It is not a fixed rate card. It recommends a price band and explains it. It does not change your prices on its own.
2. Protected capacity is held for higher-paying late buyers. protection_levels tells you how many rooms, seats or slots to hold back, and when it is safe to release them for sale.
3. You see how demand responds to your price. estimate_elasticity estimates how sales change when your price changes, from your own history. When that history has no price variation, the agent says so instead of giving a confident answer.
4. Overbooking comes with the risk shown. recommend_overbooking and optimal_overbooking set how many extra bookings to take for no-shows. show_rate_estimate and denied_service_risk show the probability and the expected number of customers you may have to turn away. The risk is never hidden behind the gain. For clinics, overbooking is capped and should never be applied to urgent or critical appointment types.
5. Booking pace tells you when to act. watch_booking_pace compares this period with the usual curve — for example, “This weekend is booking 30% ahead of the usual curve” — so a price move happens while it still matters.
6. Every recommendation can be explained. explain_recommendation shows the logic and the assumptions behind a price or a booking limit, so you can accept it, adjust it or reject it. The methods are Littlewood’s Rule, EMSR and binomial overbooking. In the revenue-management literature, applying methods like these has been associated with revenue gains in the range of 3–8%. Your results depend on your demand, your data and how you act on the recommendations.

That's the shift: from a fixed rate card to a price, a booking limit and an overbooking level you can explain — and the same answer every time you ask with the same data.

“How many rooms should we overbook?”

“Set fare buckets for next week”

“What price maximizes revenue?”