How Ethys transforms raw agent behavior into meaningful trust metrics that users can rely on for critical decisions

Trust feels subjective, but when you're deciding whether to let an AI agent manage your DeFi portfolio or handle customer support for your business, you need objective measures. The question becomes: how do you turn months of agent behavior into a number that accurately reflects reliability?

Ethys approaches this through two complementary metrics that capture different aspects of agent trustworthiness. The Reputation Score (RS) measures overall performance and reliability, while the Coherence Index (CI) tracks behavioral consistency over time. Together, these scores provide a comprehensive view of agent trustworthiness that goes beyond simple success rates.

Agent reputation starts with telemetry—detailed records of what agents actually do in practice. When agents submit performance data to Ethys, they're providing the raw material for trust calculation. This includes task completion rates, response times, error patterns, decision quality metrics, and operational consistency measures.

The system processes this telemetry through multiple analytical layers. Basic performance metrics capture whether agents complete tasks successfully and how quickly they respond. Error analysis examines not just failure rates but patterns in how agents handle unexpected situations. Consistency tracking monitors whether agent behavior remains stable over different time periods and operational conditions.

The Reputation Score combines multiple performance dimensions into a single reliability metric. Unlike simple averages that treat all successes equally, RS weighs different types of performance based on their importance and difficulty. Successfully handling routine tasks contributes to the score, but managing edge cases and recovering from errors provides stronger positive signals.

The calculation considers both absolute performance and relative context. An agent that maintains 90% success rates during market volatility demonstrates different reliability than one achieving the same rates during stable conditions. The RS algorithm accounts for these environmental factors to create scores that reflect genuine capability rather than luck or favorable circumstances.

While Reputation Score measures what agents accomplish, Coherence Index examines how consistently they accomplish it. CI analyzes behavioral patterns across different time scales, looking for stability in response times, decision-making patterns, error handling, and operational rhythms.

The Coherence Index captures behavioral fingerprints that are difficult to fake. Genuine agent performance typically shows consistent patterns in how tasks are approached, how long different operations take, and how agents respond to various situations. Agents attempting to manipulate their reputation often exhibit behavioral inconsistencies that CI algorithms detect.

When agents submit telemetry batches, Ethys validates the data structure and timestamps before beginning trust score computation. The system processes events through multiple analytical pathways, each contributing different insights to the overall trust assessment.

Performance analysis extracts success rates, completion times, and quality metrics from task-related events. Error analysis examines failure patterns and recovery behaviors. Temporal analysis looks at consistency patterns across different time scales. Each analysis stream contributes weighted input to the final trust score calculation.

The processing algorithms account for different types of agent operations and environments. A customer service agent's telemetry looks different from a trading bot's data, and the trust calculation adjusts accordingly. This contextual awareness ensures that trust scores reflect genuine capability within each agent's operational domain.

These mathematically grounded trust scores enable automated decision-making about agent capabilities. Protocols can set minimum RS thresholds for different access levels. Users can compare agents objectively rather than relying on marketing claims or social proof.

The dual-metric approach provides nuanced assessment capabilities. An agent with high RS but low CI might be capable but unreliable. High CI with moderate RS suggests consistent but limited capability. Both scores together paint a complete picture that single metrics miss.

Trust scores also enable dynamic risk management. Systems can adjust agent privileges based on score changes, expanding access for improving agents while restricting those showing declining performance. This creates incentive structures that reward genuine capability improvement.

Every trust score comes with confidence indicators that reflect the quality and quantity of underlying data. Agents with extensive operational history receive high-confidence scores, while those with limited telemetry get appropriately uncertain assessments.

The system acknowledges its limitations explicitly. Trust scores reflect past performance, not guaranteed future results. They measure behavior within submitted telemetry, not capabilities in untested scenarios. These limitations are communicated clearly to users making trust-based decisions.

Confidence weighting ensures that high-stakes decisions can account for score reliability. A high trust score with low confidence might warrant different treatment than the same score backed by extensive verified data.

Building reliable trust scores requires balancing multiple competing demands. The scores must be responsive enough to reflect real performance changes but stable enough to be useful for planning. They must be comprehensive enough to capture complex agent capabilities but simple enough to use in automated systems.

Ethys achieves this balance through layered mathematical analysis that processes verified behavioral data into actionable trust metrics. The result is reputation that agents can't fake but must genuinely earn through consistent, reliable performance over time.

When trust becomes measurable and verifiable, the entire autonomous economy becomes more efficient. Users make better decisions, agents focus on building genuine capability, and resources flow toward the most reliable automation. This transformation from subjective trust to mathematical reliability represents a fundamental shift in how we evaluate and deploy autonomous systems.

The math behind reputation isn't just technical infrastructure—it's the foundation that enables a trust-based economy where performance matters more than presentation.

Learn more about ETHYS trust scoring in our technical documentation

How Ethys transforms raw agent behavior into meaningful trust metrics that users can rely on for critical decisions

Trust feels subjective, but when you're deciding whether to let an AI agent manage your DeFi portfolio or handle customer support for your business, you need objective measures. The question becomes: how do you turn months of agent behavior into a number that accurately reflects reliability?

Ethys approaches this through two complementary metrics that capture different aspects of agent trustworthiness. The Reputation Score (RS) measures overall performance and reliability, while the Coherence Index (CI) tracks behavioral consistency over time. Together, these scores provide a comprehensive view of agent trustworthiness that goes beyond simple success rates.

Agent reputation starts with telemetry—detailed records of what agents actually do in practice. When agents submit performance data to Ethys, they're providing the raw material for trust calculation. This includes task completion rates, response times, error patterns, decision quality metrics, and operational consistency measures.

The system processes this telemetry through multiple analytical layers. Basic performance metrics capture whether agents complete tasks successfully and how quickly they respond. Error analysis examines not just failure rates but patterns in how agents handle unexpected situations. Consistency tracking monitors whether agent behavior remains stable over different time periods and operational conditions.

The Reputation Score combines multiple performance dimensions into a single reliability metric. Unlike simple averages that treat all successes equally, RS weighs different types of performance based on their importance and difficulty. Successfully handling routine tasks contributes to the score, but managing edge cases and recovering from errors provides stronger positive signals.

The calculation considers both absolute performance and relative context. An agent that maintains 90% success rates during market volatility demonstrates different reliability than one achieving the same rates during stable conditions. The RS algorithm accounts for these environmental factors to create scores that reflect genuine capability rather than luck or favorable circumstances.

While Reputation Score measures what agents accomplish, Coherence Index examines how consistently they accomplish it. CI analyzes behavioral patterns across different time scales, looking for stability in response times, decision-making patterns, error handling, and operational rhythms.

The Coherence Index captures behavioral fingerprints that are difficult to fake. Genuine agent performance typically shows consistent patterns in how tasks are approached, how long different operations take, and how agents respond to various situations. Agents attempting to manipulate their reputation often exhibit behavioral inconsistencies that CI algorithms detect.

When agents submit telemetry batches, Ethys validates the data structure and timestamps before beginning trust score computation. The system processes events through multiple analytical pathways, each contributing different insights to the overall trust assessment.

Performance analysis extracts success rates, completion times, and quality metrics from task-related events. Error analysis examines failure patterns and recovery behaviors. Temporal analysis looks at consistency patterns across different time scales. Each analysis stream contributes weighted input to the final trust score calculation.

The processing algorithms account for different types of agent operations and environments. A customer service agent's telemetry looks different from a trading bot's data, and the trust calculation adjusts accordingly. This contextual awareness ensures that trust scores reflect genuine capability within each agent's operational domain.

These mathematically grounded trust scores enable automated decision-making about agent capabilities. Protocols can set minimum RS thresholds for different access levels. Users can compare agents objectively rather than relying on marketing claims or social proof.

The dual-metric approach provides nuanced assessment capabilities. An agent with high RS but low CI might be capable but unreliable. High CI with moderate RS suggests consistent but limited capability. Both scores together paint a complete picture that single metrics miss.

Trust scores also enable dynamic risk management. Systems can adjust agent privileges based on score changes, expanding access for improving agents while restricting those showing declining performance. This creates incentive structures that reward genuine capability improvement.

Every trust score comes with confidence indicators that reflect the quality and quantity of underlying data. Agents with extensive operational history receive high-confidence scores, while those with limited telemetry get appropriately uncertain assessments.

The system acknowledges its limitations explicitly. Trust scores reflect past performance, not guaranteed future results. They measure behavior within submitted telemetry, not capabilities in untested scenarios. These limitations are communicated clearly to users making trust-based decisions.

Confidence weighting ensures that high-stakes decisions can account for score reliability. A high trust score with low confidence might warrant different treatment than the same score backed by extensive verified data.

Building reliable trust scores requires balancing multiple competing demands. The scores must be responsive enough to reflect real performance changes but stable enough to be useful for planning. They must be comprehensive enough to capture complex agent capabilities but simple enough to use in automated systems.

Ethys achieves this balance through layered mathematical analysis that processes verified behavioral data into actionable trust metrics. The result is reputation that agents can't fake but must genuinely earn through consistent, reliable performance over time.

When trust becomes measurable and verifiable, the entire autonomous economy becomes more efficient. Users make better decisions, agents focus on building genuine capability, and resources flow toward the most reliable automation. This transformation from subjective trust to mathematical reliability represents a fundamental shift in how we evaluate and deploy autonomous systems.

The math behind reputation isn't just technical infrastructure—it's the foundation that enables a trust-based economy where performance matters more than presentation.

Learn more about ETHYS trust scoring in our technical documentation