Build a Multi-Agent Model Selection Committee with LUMOS: Automate AI Evaluation for Operations

Why Operational Teams Need a Model Selection Committee Manual evaluation of new AI models is slow, error-prone, and often disconnected from real operational constraints. Procurement teams struggle to predict how a model will handle sourcing queries; supply chain managers fear unexpected latency spikes; ITSM leaders worry about downtime. Without a structured process, model adoption becomes a gamble—one that can disrupt stability and waste budget. A multi-agent model selection committee addresses this by automating evaluation across the dimensions that matter most to operations: task accuracy, latency, cost, and generative engine visibility. This framework turns model evaluation from a manual bottleneck into a repeatable, auditable workflow. Designing the Multi-Agent Committee with LUMOS LUMOS serves as the orchestrator for a set of specialized agents, each responsible for one evaluation d

imension. The committee typically includes: - Benchmarking Agent : Runs standardized test cases derived from your operational workflows. - Latency Agent : Simulates production loads to measure response times under stress. - Cost Agent : Tracks token consumption, API call frequency, and total cost of ownership. - GEO Impact Agent : Simulates how the model’s outputs might be cited by generative search engines. - Scoring Agent : Aggregates results into a weighted matrix and produces a go/no-go recommendation with rollback triggers. LUMOS enables these agents to communicate, share intermediate results, and iterate. The committee can be triggered automatically on new model releases or run on a schedule for continuous monitoring. Step 1: Deploying Benchmarking Agents on Your Operational Tasks To evaluate a model, you must first define what “good” looks like. For procurement, that might mean co

rrectly extracting line items from purchase orders. For supply chain, it could be predicting lead times. For ITSM, resolving incident tickets with accurate root-cause analysis. Mapping each workflow to standardized test cases involves: 1. Task Decomposition : Break each operational process into discrete, measurable tasks. 2. Test Case Creation : For each task, create a set of input-output pairs (at least 100) that represent typical edge cases. 3. Agent Configuration : In LUMOS, define the Benchmarking Agent to iterate over these test cases and record accuracy, F1 scores, and failure modes. The agent runs the model against these cases without altering production data, ensuring safety. Results are stored in a shared dataset for later scoring. Step 2: Measuring Latency and Cost in Real-World Conditions Latency and cost are critical for operational environments. A model that answers sourcing

questions in 10 seconds may be unacceptable when your team processes thousands of queries per hour. Similarly, a model with high per-token cost can blow budgets. The Latency Agent uses LUMOS’s built-in traffic simulation to mimic production load—ramping up concurrent requests and measuring p50/p95/p99 response times. For example, as of May 2026, a typical large language model running on cloud GPUs might show p95 latency of 800–1200 ms for complex queries, while a smaller distilled model could be under 300 ms. The Cost Agent tracks actual token spend (input + output) across the test runs, factoring in batch discounts if applicable. It computes total cost per 1,000 operational tasks and estimates monthly spend based on your projected volume. All figures should be cross-referenced with the vendor’s official pricing page. Step 3: Simulating Generative Engine Citation Impact (GEO) Generative

Engine Optimization (GEO) is becoming a key factor for enterprise AI visibility. When your model’s outputs are cited by search engines—either embedded in AI-generated answers or as references—it can drive organic traffic and credibility. The GEO Impact Agent simulates this by: - Generating sample outputs from the model for typical user queries. - Analyzing how those outputs align with potential citation patterns (e.g., structured data, factual accuracy, authority signals). - Scoring the likelihood of citation on a 0–100 scale based on known search engine ranking signals (as of early 2026). This simulation is not a guarantee but a predictive indicator. Models that score higher in GEO impact are more likely to contribute to your organization’s visibility in generative search results. Building the Scoring Matrix and Go/No-Go Logic The Scoring Agent combines results from all other agents in

to a weighted matrix. Weights are configurable but a balanced default might be: - Task Accuracy : 35% - Latency (p95) : 20% - Total Cost per 1K Tasks : 25% - GEO Impact Score : 10% - Other (e.g., explainability, bias metrics) : 10% Each dimension is normalized to a 0–100 scale. The composite score d