Glossary
Ensemble Agent
An AI agent that combines multiple models or reasoning approaches to make decisions, aggregating diverse outputs for improved accuracy.
What is Ensemble Agent?
Ensemble agents leverage multiple models, prompting strategies, or reasoning paths to produce more robust results than any single approach. Common techniques include majority voting across models, confidence-weighted averaging, or specialized models handling different aspects of a problem. Ensembles reduce reliance on any single model's biases or weaknesses, often improving accuracy and robustness.
The tradeoff is increased computational cost and complexity, as multiple inferences are required per decision. Effective ensembles carefully select diverse models or approaches that make different types of errors, so their combination is stronger than components. Ensemble agents may use model routing to apply the best-suited model for each input, or consult all models and synthesize responses.
Example
A medical diagnosis agent combines three specialized models: one trained on radiology reports, one on patient symptoms, and one on lab results. It aggregates their independent assessments, flagging cases where models disagree for human specialist review while automatically processing cases with consensus.
How Signet addresses this
Signet's Quality dimension rewards ensemble approaches that demonstrably improve accuracy over single-model baselines. However, Reliability also considers the complexity and potential failure modes of managing multiple models, balancing quality gains against operational risk.
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