INFORMS Open Forum

Dear Colleagues,

A persistent challenge in applied operations research is the operational translation layer: how do we place sophisticated optimization models directly into the hands of on-property decision-makers without incurring prohibitive training or software integration overhead?

To explore this, I have built AERA, a domain-specific AI Agent designed as a working implementation of the Yield Equilibrium Protocol<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element><response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element>. The branding logo and interface layout are captured below:<sources-carousel-inline ng-version="0.0.0-PLACEHOLDER" _nghost-ng-c3359988372=""> </sources-carousel-inline>

Link to Beta Access: <response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element>https://udify.app/chat/RPHcxQOkGQZD4VsL<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element>

Rather than acting as a simple, text-based conversational wrapper, AERA functions as an intelligent orchestration layer. It uses a structured prompt architecture and a diagnostic engine to interpret natural language, parse intent, and route the user to deterministic mathematical modules:

1. Group Displacement Analysis: Calculating transient opportunity costs against incoming group RFPs.

2. Room Rate Analysis: Evaluating price elasticity and contribution margins across Promotional, Corporate, and Transient Agent (TA) segments.

3. Segmentation Analysis & Performance: Extracting variance data to measure true net yield by channel.

4. Budget Feasibility Check: Running capacity-constrained checks to validate stochastic revenue targets.

5. Market Insights & Situational Intelligence: Synthesizing forward-looking macroeconomic metrics.

Systemic Routing to Algorithmic Crisis Recovery (ACR)

Where standard conversational models hallucinate or fail under extreme structural breaks, this agent is programmed with hard algorithmic guardrails. Under anomalous conditions (e.g., severe exogenous demand shocks), AERA bypasses standard yield-maximization algorithms and executes an Algorithmic Crisis Recovery (ACR) protocol<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element><response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element>. It recalculates a cost-derived Minimum Acceptable Rate (MAR) based on step-variable physical wing consolidation and sequences an optimization matrix for workforce retention against counterfactual replacement friction<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element><response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element>.<sources-carousel-inline ng-version="0.0.0-PLACEHOLDER" _nghost-ng-c3359988372=""> </sources-carousel-inline>

Let's Discuss: Agentic Orchestration in Revenue Management

As we look at the evolution of automated hospitality distribution, the transition from rigid software UIs to autonomous, agentic workflows feels inevitable. I would highly value the perspective of this community on a couple of foundational questions:

• Deterministic vs. Stochastic Balance: When building AI agents for revenue management, how are you structuring the boundaries to ensure the agent uses LLM reasoning only for natural language intent parsing, while strictly relying on deterministic scripts for the actual optimization math?

• The API Bottleneck: In specialized property-level applications (like tracking BMS utility loads or HRIS payroll metrics), what strategies are proving most effective for managing data latency when agents query highly siloed legacy databases<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element><response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element>?<sources-carousel-inline ng-version="0.0.0-PLACEHOLDER" _nghost-ng-c3359988372=""> </sources-carousel-inline>

I look forward to your insights, technical critiques, and shared experiences on bringing agentic architectures to field operations.

Sincerely,

N. P. Gayan Nugawela

<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element><response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element><sources-carousel-inline ng-version="0.0.0-PLACEHOLDER" _nghost-ng-c3359988372=""> </sources-carousel-inline>

<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element>Connect with me on LinkedIn<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element> 

Independent Researcher in Hospitality Revenue Management and Sustainability Governance<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element><response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element><sources-carousel-inline ng-version="0.0.0-PLACEHOLDER" _nghost-ng-c3359988372=""> </sources-carousel-inline>