Evaluation sets
Turn real business cases into evaluation sets covering normal, edge and risk scenarios.
Agent Evaluation & Self-Evolution Platform
MateEvolve
Continuously evaluate agents and learn from execution feedback — digital employees that keep getting better.
MateEvolve is the platform for agent evaluation and self-evolution. It builds evaluation sets and criteria, measures accuracy, stability and safety automatically, and distills improvements from real execution traces and human feedback to optimize prompts, skills and policies — a measurable evolution loop.
Capabilities
Core capabilities
Automated evaluation
Rules, model-as-judge and human sampling measure accuracy, stability and safety at scale.
Execution tracing
Every reasoning step, tool call, latency and cost recorded — issues located and reproducible.
Feedback learning loop
User ratings and human corrections are distilled into concrete improvement suggestions.
Policy & prompt optimization
Experiment with prompts, skill combinations and model choices and ship the winner.
Version comparison & canary
A/B comparison and canary releases make impact quantifiable and risk controllable.
Use cases
Use cases
Pre-release quality gates
Agents must pass the evaluation baseline before going live.
Model upgrade assessment
Quantify the impact of switching or upgrading LLMs with business evaluation sets.
Continuous optimization
Tune with real usage feedback so digital employees improve steadily.
Ecosystem
Where it fits
MateEvolve runs on the EASEOS foundation and shares its runtime protocol, skill system and governance with every other product — clear boundaries, interoperable capabilities.
Products
Work entry
Foundation
CloudOn-premise devicesDomestic tech stacksDatabasesBusiness systems
FAQ
FAQ
Why do agents need evaluation?
Agent output is non-deterministic. Without evaluation you can't know if it is reliable, or whether a change made it better or worse.
Can self-evolution go out of control?
Every optimization is grounded in evaluation results, validated by experiments and canary releases, and critical changes can require approval.
Where does MateEvolve get execution data?
From traces recorded by the EASEOS Evaluation / Observability layer — no duplicate instrumentation in each product.
Related products
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