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2025

Outcome-Driven Agent Evaluation (Hive)

Evaluation patterns for agents that must improve real outcomes.

Exploration and extension of the Hive framework for outcome-driven agent development, focusing on how teams iterate when success is measured by business results rather than single-turn benchmarks.

Stack

Python
Agent Frameworks
Evaluation Design
OSS
Apache 2.0

Proof metrics

Repository
Public GitHub fork
Lens
Outcome loops vs. toy task accuracy
Use
Research and internal eval experiments

Problem

Most agent demos optimize for demo-quality replies, not sustained reliability in production workflows.

Teams need structure for iterating prompts, tools, and policies when the scorecard is operational impact.

Solution

Worked with Hive's outcome-oriented abstractions to stress-test evaluation habits for agent systems.

Used the fork as a sandbox for methodology that complements production Claude agent work.

Architecture

Architecture diagram (add image or Mermaid export when ready)

Python framework surfaces for defining agent behaviors and measurement hooks.

Separation between execution, evaluation, and iteration workflows.

Outcomes

Sharper internal discipline for judging agent changes before they reach customer-facing products.

Public footprint in the agent evaluation conversation beyond application code alone.

Links & artifacts

GitHub ForkUpstream HiveContact

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Joseph Rwanda

AI Engineer · LLM systems architect · Nairobi, Kenya

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