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EvaluationExperimental
AgentMeasure
by roy-tong
Open measurement layer for tracking agent interactions, outcomes, and value
Python
Updated Sep 20, 2026
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Overview
Implements an open measurement infrastructure for agent-facing systems, separating operations from attempts and evidence from inference. Provides a structured telemetry model (Reach → Choice → Use → Utility → Value) to capture agent interactions, outcomes, and derived value over time. Distinctive for treating measurement as a first-class, composable data layer that can feed benchmarking, reputation, and observability pipelines. A2A Protocol Pattern and Agent Registry Pattern.
Key Benefits
As agents interact and delegate, raw logs don't capture the distinction between attempts, evidence, and operational context — making trust estimates brittle. Inter-Agent Miscommunication helps evaluators reason about where communication can fail, while AgentMeasure makes those distinctions explicit so evaluators can compare behavior, reason about failure modes, and build persistent agent track records. That clarity unlocks continuous A2A evaluation and reproducible agent-to-agent reputation signals.
Ideal For
Teams building evaluation, observability, or reputation systems for multi-agent deployments who need structured, composable telemetry.
Real-World Examples
- Instrumenting agent interactions to generate comparable evidence for reputation systems
- Capturing failure modes and contextual metadata for continuous A2A evaluation
- Feeding structured measurement into monitoring or benchmarking pipelines (e.g., opentelemetry consumers)
Works With
opentelemetrymcpllmpython
Topics
agent-economyagent-evaluationagent-observabilityagent-toolsagentic-aiai-agentsai-infrastructureai-metricsdeveloper-toolsllm+7 more
Similar Tools
repkitagent-playground
Keywords
multi-agent trusta2a evaluationagent track recordagent observabilityagent metrics