Back to Ecosystem Pulse
EvaluationExperimental
ai-agent-eval-harness
by najeed
Scenario-driven evaluation harness for multi-agent A2A testing
Python
Updated Aug 25, 2026
Share:
Overview
Provides an open-source evaluation and verification harness for multi-agent business workflows. It runs scenario-driven tests and checks agent outputs against business rules, allowing reproducible A2A evaluation across pipelines. It distinguishes itself with industry-focused workflow templates and assertions for both functional correctness and process-level failures. A2A Protocol Pattern
Why It Matters
As agents coordinate more autonomously, systematic A2A evaluation is necessary to surface failure modes and build agent track records. Until now many teams relied on ad-hoc tests — this harness brings repeatable, scenario-based verification and logging so teams can measure agent reliability and compare behaviors over time. That makes reputation signals and continuous agent evaluation practical in production-like workflows. The approach aligns with structured workflows like the Model Context Protocol (MCP) Pattern.
Ideal For
Teams validating agent collaborations in industry workflows who need repeatable A2A evaluation and verifiable business-rule checks. It pairs well with the Planning Pattern to design robust scenario templates, and it can leverage the Agent Registry Pattern to manage participating agents.
Applications
- Validate multi-agent business workflows against compliance and business rules
- Run reproducible A2A evaluation scenarios to surface agent failure modes
- Record agent interactions and build a track record for reliability comparisons
Topics
agent-debuggingagent-observabilityagent-testingagent-verificationagentopsai-agent-toolsai-agentsai-agents-frameworkai-reliabilitybenchmarking+10 more
Similar Tools
agent-playgroundagent-arena
Keywords
multi-agent trustA2A evaluationagent track recordagent-to-agent evaluation