Agenteval
AI & ML interests
Agenteval AI and AGI Company
Recent Activity
AgentEval
Measure what agents actually do — not just what they say.
Evaluation for AI systems that act
AgentEval is an independent Hugging Face organization focused on evaluating AI agents as complete systems.
A strong agent should do more than produce a good-looking answer.
It should:
- understand the task
- choose the right tools
- use them correctly
- recover from failure
- stay within constraints
- complete the task
- do so efficiently
- produce a verifiable result
That requires a different evaluation mindset.
Outcome first. Trace second. Model score third.
What should an agent evaluation measure?
A useful agent evaluation can include several layers:
TASK
↓
PLAN
↓
TOOL CHOICE
↓
TOOL EXECUTION
↓
RECOVERY
↓
FINAL RESULT
↓
VERIFICATION
AgentEval focuses on the full path.
01 · Task Success
The most important question:
Did the agent complete the task?
Possible metrics:
- success / failure
- partial completion
- goal coverage
- final answer correctness
- constraint compliance
- completion consistency across repeated runs
A fluent answer is not enough if the task was not completed.
02 · Tool Use
Agents increasingly depend on external tools.
Evaluation can inspect:
- correct tool selection
- valid arguments
- unnecessary tool calls
- failed tool calls
- retries
- fallback behavior
- sequence of tool usage
- tool-result interpretation
03 · Traces
Agent behavior becomes easier to understand when the execution trace is visible.
Useful trace elements may include:
- steps
- tool calls
- model calls
- errors
- retries
- timestamps
- latency
- token usage
- cost
- intermediate state
- final result
A final answer can hide a bad process.
A trace makes the process inspectable.
04 · Reliability
One successful run does not prove reliability.
AgentEval is interested in repeated execution.
Possible metrics:
- pass rate
- variance across runs
- retry rate
- failure mode frequency
- deterministic vs. unstable behavior
- recovery success
- consistency under perturbation
05 · Efficiency
An agent can complete a task and still be inefficient.
Possible efficiency metrics:
- number of steps
- tool calls
- model calls
- latency
- token usage
- estimated cost
- redundant actions
- unnecessary retries
The best agent is not always the one with the highest raw capability.
Sometimes it is the one that completes the task with fewer resources.
06 · Safety & Constraint Following
Agent evaluations may also inspect whether a system stays within defined boundaries.
Examples:
- allowed tools only
- no unauthorized actions
- no secret leakage
- no unsafe command execution
- approval required before sensitive actions
- adherence to role or workflow constraints
Possible Spaces
Agent Task Evaluator
Compare expected outcomes with an agent's final result and trace.
Tool-Call Grader
Check whether an agent selected the correct tool and valid parameters.
Trace Inspector
Upload an agent trace and inspect steps, retries, latency, failures, and cost.
Reliability Lab
Run repeated synthetic evaluations and compare consistency.
Agent Efficiency Score
Measure task completion against steps, token usage, tool calls, and runtime.
Failure Mode Explorer
Analyze why an agent failed and classify recurring failure patterns.
Multi-Agent Evaluator
Compare collaboration quality between planner, executor, reviewer, and verifier roles.
Agent Benchmark Builder
Create custom task sets with expected outputs and evaluation criteria.
Tool-Use Benchmark
Score agents on structured tool-selection and execution tasks.
Agent Regression Check
Compare two agent versions on the same evaluation set.
Possible Datasets
AgentEval can host datasets designed specifically for evaluation.
Examples:
agent-eval-tasks
tool-use-evals
agent-failure-cases
agent-traces
multi-agent-tasks
recovery-scenarios
constraint-following-tests
agent-regression-suite
A useful evaluation dataset may include:
- task
- environment
- available tools
- expected result
- allowed actions
- prohibited actions
- success criteria
- reference trace
- scoring rubric
Possible Models
Models may support evaluation tasks such as:
- trace classification
- failure-mode detection
- tool-call validation
- task-success prediction
- reward modeling
- output grading
- execution-quality scoring
- anomaly detection in agent traces
A simple evaluation record
{
"task": "Find the cheapest valid route",
"success": true,
"tool_calls": 4,
"failed_tool_calls": 1,
"steps": 7,
"latency_seconds": 12.4,
"estimated_cost": 0.031,
"constraint_violations": 0
}
One record is useful.
Hundreds of repeated records become a benchmark.
Core Evaluation Dimensions
| Dimension | Core Question |
|---|---|
| Success | Did the agent complete the task? |
| Correctness | Was the result right? |
| Tool Use | Were the right tools used correctly? |
| Reliability | Does it work repeatedly? |
| Efficiency | How much time, cost and work did it require? |
| Recovery | Can it recover from failure? |
| Safety | Did it respect constraints? |
| Trace Quality | Is the execution understandable and auditable? |
AgentEval Scorecard
A practical evaluation may combine multiple signals:
Task Success 40%
Tool Accuracy 20%
Reliability 15%
Efficiency 10%
Recovery 10%
Constraint Safety 5%
The exact weighting depends on the use case.
AgentEval does not promote one universal score.
Different agents require different evaluation criteria.
Why agent evaluation is different
Traditional model evaluation often asks:
How good is the answer?
Agent evaluation may need to ask:
Did the system complete the task correctly, efficiently, safely, and repeatably?
That distinction matters.
An agent is a system.
Its quality depends on more than one model response.
Principles
Measure outcomes
A convincing explanation does not equal successful execution.
Inspect traces
Execution history matters.
Repeat evaluations
One run is not enough.
Separate capability from reliability
An agent may be capable but unstable.
Include cost and latency
Operational quality matters.
Evaluate tool behavior
Tool misuse can invalidate an otherwise correct result.
Make failure visible
Benchmarks should help explain failure, not hide it.
Technology Directions
Projects may use:
- Hugging Face Spaces
- Hugging Face Datasets
- structured evaluation sets
- agent traces
- tool-calling logs
- JSON evaluation records
- Python
- JavaScript
- benchmark harnesses
- scoring pipelines
- LLM-as-a-judge experiments
- deterministic validators
- regression testing
- observability data
Who is AgentEval for?
AgentEval may be useful for:
- agent developers
- AI engineers
- eval teams
- platform engineers
- researchers
- QA teams
- MLOps teams
- startups
- enterprise AI teams
- tool developers
- open-source contributors
Important Note
Projects published here are primarily intended for:
- research
- development
- benchmarking
- education
- prototyping
- technical experimentation
Evaluation scores are not universal guarantees of:
- reliability
- safety
- production readiness
- correctness
- regulatory compliance
- suitability for high-impact use
Agent systems should be evaluated in the environment and context in which they are actually used.
Independent Organization
AgentEval is an independent Hugging Face community organization.
It is not an official benchmark authority, certification body, standards organization, model provider, or Hugging Face organization.
The name AgentEval reflects the technical focus:
evaluation for AI systems that plan, act, use tools, and complete tasks.