Benchmark against real work
Evaluate agents on the decisions that matter in your systems, not demo prompts.
Governance & trust
Score and benchmark agents before they reach production.
AgentEvaluation helps teams measure enterprise AI agents against real workflows — so trust is earned with evidence before autonomy rises.
- Pre-production scoring
- Regression checks
- Decision-trace ready
AgentEvaluation
Governance & Trust
Product site
agentevaluation.ai
Live
Context OS · decision harness
- Context compilation
- Dual-gate policy
- Decision traces
Score and benchmark enterprise AI agents before and after they go live.
ElixirData parent
Hub · /agents
Parent
Agent directory
Every agent ElixirData parents
The rest of the governed family — evaluation, analytics, search, labeling and trust — each compiled, checked and traced on Context OS.
Browse by cluster, then open the agent that matches the decision you need to govern.
Governance & Trust
01 / 03Data & Analytics
02 / 03AI Data & Training
03 / 03Why AgentEvaluation
One decision harness for this agent
AgentEvaluation is not a standalone demo. It compiles context, passes dual-gate policy checks and writes decision traces back into Context OS.
Catch drift early
Compare runs over time so quality regressions show up before users do.
Evidence for trust graduation
Give risk and product teams a shared scorecard for moving from shadow to autonomy.
Shared business context
AgentEvaluation reasons on validated, versioned enterprise truth — entities, rules, authority and history.
Governed on Context OS
Outbound actions from AgentEvaluation are allowed, modified, escalated or blocked before systems of record.
Part of ElixirData
Parented on the /agents hub in the Governance & Trust cluster, with XenonStack as the corporate home.
Where it fits
AgentEvaluation for the work that needs control
Score and benchmark enterprise AI agents before and after they go live. Put Context OS underneath so every action is checked and traced.
Before go-live
Score agents on real workflows so trust is earned before autonomy rises.
After model changes
Re-run evaluations when prompts, tools or models shift.
Trust graduation
Give risk and product a shared scorecard from shadow to production.
With AgentQA
Pair scoring with continuous QA so quality and benchmarks stay aligned.
How the network links
Parent once. Sibling by cluster.
How AgentEvaluation sits in the ElixirData network — relevance over volume, without a mesh footer of every sister site.
| What not to do | What ElixirData does | |
|---|---|---|
| Product site | Present AgentEvaluation as an orphan brand | agentevaluation.ai with ElixirData as platform parent |
| Parent link | No path back to the agents hub | One clear path to ElixirData /agents |
| Siblings | Dump every sister site in the footer | Directory of the other seven agents by cluster — Governance & Trust first |
| Brand | Hide the corporate relationship | Part of ElixirData · A XenonStack company |
| Runtime | Let AgentEvaluation decide without shared policy | Context OS compiles context, checks policy, stores traces |
How to start
Put AgentEvaluation on the decision
Start where risk or value is highest for this agent, then widen once decision traces prove accuracy.
Choose
Confirm AgentEvaluation matches the decision you need to govern or accelerate.
Connect
Wire Context OS to the systems, knowledge and policies this agent needs.
Shadow
Run alongside people while traces show what the agent would have done.
Scale
Raise autonomy where earned and add sibling agents from the Governance & Trust cluster.
FAQ
AgentEvaluation, answered
Straight answers on what this agent does, how it fits Context OS, and how it links in the network.
AgentEvaluation scores and benchmarks enterprise AI agents against defined workflows so teams can prove quality before and after go-live.
Evaluations can sit on the same decision harness: compiled context, policy checks and traces that make results auditable.
Platform, QA and risk teams that need evidence before raising agent autonomy.
Use the ElixirData brand bar, link once to /agents, and show the other agents by cluster — not a full mesh footer.
Put AgentEvaluation on Context OS
We map the decisions this agent should own, connect Context OS underneath, and keep every action checked and traced.