Evaluations
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At the core of Amigo's implementation methodology is a holistic approach to evaluations that transforms subjective assessments into objective, quantifiable measurements. Rather than relying solely on human feedback, which can be slow and inconsistent, we enable LLM-powered evaluation to generate results that remain consistent regardless of who conducts the tests.
This system creates precisely calibrated evolutionary pressure that drives continuous agent improvement. It targets the agent's entire Memory-Knowledge-Reasoning (M-K-R) cognitive cycle to ensure that memory is effectively recalled and recontextualized, knowledge is appropriately activated and applied, and reasoning is sound and aligned with strategic objectives. The goal is a cyclical optimization of the unified M-K-R system.
The Arena is a controlled environment where AI agents evolve under carefully designed pressures that align with organizational goals. The four key components of this process are:
Multidimensional Metrics
Personas and Scenarios
Programmatic Simulations
Continuous Improvement
The failure of conventional AI evaluation systems is partially caused by focusing on single-turn response quality with simple 'good-or-bad' judgments. In reality, real-world scenarios contain many interrelated factors; for a clinical setting this includes medical accuracy, empathy, guideline adherence, risk assessment, and more. To serve these complex use cases, we designed the Arena to assess the agent's performance across entire conversations and complex scenarios.
These end-to-end evaluations are fundamentally different in both scope and depth:
Full Conversation Assessment: Rather than evaluating isolated responses, our system analyzes complete conversation flows, examining how effectively agents navigate complex, multi-turn interactions.
Computational Investment: Our judges and simulators use significantly more time and computational resources (reasoning tokens) to fully explore problem spaces, probing agents in creative and edge-case scenarios that reveal subtle behavioral patterns. This is a critical distinction from simple response pair evaluations used by many systems.
Intelligent Challenge Generation: By defining both personas and scenarios, simulators gain the latitude to intelligently push system boundaries, creating dynamic challenges that uncover edge cases a simpler evaluation might miss.
Domain-Specific Intelligence: The framework leverages the data foundation and research expertise of our domain expert partners to build problem space simulators and judges that accurately reflect real-world complexities and requirements.
Model Specialization: Simulators and judges may employ custom models, stronger models, or domain-specific models to apply appropriate evolutionary pressure beyond what the primary agent model can achieve alone.
This approach provides a substantially richer understanding of agent capabilities than traditional evaluation methods, enabling targeted improvements that address the nuanced requirements of enterprise deployments.
See below for a breakdown of each step in the process and how they fit together.
First, we work with your team to define metrics to translate qualitative expert judgments into quantifiable, objective success criteria. For instance, rather than instructing an AI doctor to "demonstrate good bedside manner," we identify specific behaviors—within areas like accuracy in medical diagnoses or clarity in patient communication—that can be consistently measured across millions of interactions.
Conventional measurement systems test one simple metric at a time, often optimizing for academically-defined AI performance benchmarks. In reality, clinical scenarios contain many interrelated factors: medical accuracy, empathy, guideline adherence, risk assessment, and more. For this reason, we built our metrics system to measure holistic outcomes that balance all these critical dimensions, ensuring agents perform effectively in the reality of healthcare interactions.
Another key insight about good metrics is that they can’t be static. They need adapt as new scenarios and organizational priorities emerge, maintaining relevance and precision over time. By grounding evaluations in objective criteria, we ensure every improvement is targeted, measurable, and aligned with organizational needs.
Example groupings of different metrics types:
Metrics alone aren't sufficient without a rigorous environment for testing. This is where simulations come in. We build the guardrails for comprehensive, realistic simulations that mimic the complexity of real-world interactions. Each simulation incorporates:
Personas: detailed representations of the people who will interact with the agent
Name: Unique identifier for the simulated user
Role: Professional or contextual role (e.g., patient, student)
Background: Detailed contextual information about communication style and knowledge
Behavioral Patterns: Defining characteristics that guide how the simulator will challenge the agent
Scenarios: designed to explore challenging conditions and edge cases
Objective: Goal and situation being simulated
Instructions: Detailed guidance for simulation behavior
Edge Case Coverage: Intentional design to explore challenging situations
Each persona is paired with multiple scenarios, creating a comprehensive persona/scenario matrix. Each pairing in this matrix will be re-run across conversational variations to stress test robustness under different conditions, ensuring agents aren’t able to pass tests by chance. The result is a much more comprehensive assessment of agent capabilities, providing clear areas for targeted improvements.
Designing good simulations also requires deep domain knowledge. Through our partnership model, we leverage the data foundation of your organization's domain specialists to create evaluation environments that accurately reflect real-world complexities. This collaboration ensures that personas embody authentic user behaviors, scenarios encompass the full range of situations encountered in practice, and edge cases reflect actual challenges rather than theoretical concerns.
Now that we have defined success metrics and developed nuanced personas and scenarios, we can begin to conduct adversarial testing at scale. Our programmatic simulation system resolves the bottleneck caused by relying solely on human evaluators, unlocking rapid development cycles while enhancing rigorous quality standards that would be impossible to achieve through manual methods.
Simulations combine personas and scenarios with with specific metrics to evaluate critical agent behaviors in a controlled environment. Our simulation system also unlocks multi-interaction evals (most systems typically do only single-message eval). This means in a single simulation, the agent may have a 100+ message conversation back and forth before being evaluated. This allows us to fully saturate scenarios that are meant to represent complex conversations (e.g., a patient talking about their symptoms).
To run our simulations, we use dedicated simulator and judge agents that are themselves reasoning models. These agents run on domain-specialized or more powerful foundation models and are equipped with 10-50× more reasoning tokens than the primary agent, to ensure they make good judgments. Thousands of automated simulations are then run against the primary agent to benchmark performance against organization-defined thresholds; by focusing testing on the dimensions that drive the greatest strategic value, organizations can establish a clear and specific performance delta over their competition.
Agents are rigorously challenged, exposing vulnerabilities and enabling iterative improvements. These evaluations produce a statistically significant confidence score, and patterns can then be visualized via capability heat maps and performance reports. Our evaluators transparently display their reasoning, allowing domain experts and safety teams to audit the logic behind each assessment. This transparency helps identify and correct misalignments quickly, fostering trust and ensuring evaluations remain firmly grounded in professional standards. In conjunction with human testing to provide oversight, programmatic evaluations provide objective insights on safety and performance at full deployment scale.
A significant advantage of this approach is that our simulator and judge models explicitly show their reasoning process when creating scenarios or evaluating primary agent performance. This transparency provides several critical benefits:
Precise Misalignment Identification: Debugging AI systems has traditionally been challenging due to the difficulty of reproducing exact contexts that exposed problems. The Arena solves this through perfect reproducibility at scale. When evaluations produce unexpected results, we can examine the reasoning chain that led there, pinpointing exactly where misalignments occurred.
Rapid Iteration Cycles: With clear visibility into simulator and judge reasoning, improvements can be targeted precisely at the specific reasoning steps that need refinement, rather than making broad, unfocused changes.
Reasoning Verification: Domain experts can verify that the simulator and judge reasoning processes align with expert understanding, ensuring evaluations reflect genuine domain standards rather than AI biases.
Continuous Refinement: As new edge cases emerge, the explicit reasoning trails enable systematic improvement of evaluation criteria with minimal effort, creating a virtuous cycle of increasingly accurate assessment.
The final component is a structured cycle of ongoing measurement, analysis, and refinement. At regular intervals, the complete test set is re-run, ensuring consistent and current evaluation of AI agent performance. Results from these simulations are methodically analyzed against established performance baselines and strategic targets to pinpoint areas requiring attention. After targeted enhancements are made, subsequent evaluations verify whether these enhancements have effectively improved agent performance.
The Arena transforms AI evaluations and improvement from an art into a science, creating systems that consistently meet user needs while accelerating innovation through structured, data-driven processes.
Future advancements in AI, where systems might increasingly self-generate their own learning tasks and improve from verifiable environmental feedback without needing extensive human-curated datasets, could further enhance the autonomy and efficiency of the simulator and judge agents within our system. Amigo's commitment to auditable and metrics-driven evolution prepares our partners to leverage such breakthroughs.
Trend analysis reports, improvement tracking dashboards, and business impact assessments are provided to give continuous visibility into progress. This disciplined, data-driven cycle ensures that the agent consistently evolves to meet and exceed organizational objectives over time. And when performance improvements plateau, our pipeline takes over to push the agent past human ceilings.
