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EvaluationExperimental
Flowness
by Towow-ai
Evidence-driven multi-agent evaluation with sealed provenance and juried acceptance
Python
Updated Aug 8, 2026
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How It Works
Implements an evidence-driven harness for multi-agent engineering that runs parallel agents, collects sealed evidence, and uses independent juries to judge outputs. Coordinates targeted rework cycles and produces traceable acceptance decisions so each change has an auditable rationale. Distinctive features include sealed evidence capture and jury-driven acceptance instead of simple metric scores.
Why It Matters
As agents take on more autonomous and interdependent tasks, deterministic tests and raw metrics miss nuanced failure modes and provenance needs. Flowness makes agent interactions auditable and actionable by surfacing evidence, delegating targeted rework, and formalizing acceptance via juries. That approach shifts evaluation from one-off benchmarks toward a repeatable, reputation-friendly workflow for agent-to-agent evaluation and auditable and actionable continuous improvement.
Target Use Cases
Teams building and testing multi-agent workflows who need auditable evidence, structured rework, and reproducible acceptance criteria.
How It's Used
- Validate agent delegation by collecting sealed evidence and juried acceptance before deployment
- Diagnose multi-agent system failures by tracing provenance and targeted rework steps
- Run reproducible A/B comparisons where juries decide acceptance instead of single metrics
Topics
agent-evaluationagent-governanceagent-orchestrationai-agent-harnesscodex-clicoding-agentsevidence-drivenharness-engineeringhuman-in-the-loopllm-evaluation+4 more
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Keywords
multi-agent trusta2a evaluationagent-to-agent evaluationevidence-driven