Environments
Evaluate agents in replicas of production.
- Working copies of the systems your agents use
- Real agent tasks against realistic records
- Every tool call and state change graded
Agent Readiness Platform
Your business is your benchmark.
The systems your agents use. The situations they need to handle. The outcomes your business requires.
Evaluate. Simulate. Generate.
Refund agent · illustrative task simulation
CRM
Refund policyrecorded
Orders
Order 4471 · $28recorded
Gift card · $12recorded
Payments
Charge · settledrecorded
Task 4471 · standardTask 4472 · harderAgent
Verdict
Task 4471pass
Matched the known answer. On record
Task 4472fail
Agent missed it Claimed $40, policy says $28.
Illustrative task simulation. Follow one passing result and one failure before testing your own agent.
Trusted by teams building with AI
One platform
Environments. Evaluations. Synthetic Data.
Evaluate agents in replicas of production, grade every action, and generate the cases your data misses.
Evaluations
Grade what your agents actually do.
- Define what a completed task looks like
- Grade outputs, tool calls, and multi-step actions
- Compare agent versions on the same tasks
Explore evaluations No environment required
Synthetic data
Cover the edge cases your agents will face.
- Generate scenarios production has not hit yet
- Review every example against ground truth
- Feed them into environments or evaluations
Explore synthetic data Ground truth included
Use one. Use all three. Connect environments, evaluations, and synthetic data to keep testing as your agent evolves.
Environments
Evaluate agents in replicas of production.
Your agent runs real tasks against working copies of the systems it uses in production. Pick an environment to see how it is evaluated. Explore each step.
Featured environments+ more
Chapter 1 · BuildChapter 2 · RunChapter 3 · EvalChapter 4 · OptimizeChapter 5 · On record
The tools and records your agent works withFollow the agent's actionsInspect the result against your requirementsCompare the next versionReview the evidence
Start with the records and tools the task requires. Add reviewed synthetic examples to cover missing information and conflicting records.Follow the example agent as it reads records and takes action. Check whether the resulting work meets the task requirements.Inspect the action and its result. Check the records available to the agent to understand where the task went wrong.Give both agent versions the same work. Compare what they got right and wrong, then inspect the steps behind the differences.Use what you learn to improve the agent, test it again, and try new situations. Review the results before deciding what to change next.
Accounts
Account · Acme Corprecorded
Owner · Dana Parkrecorded
Environment couldn't answer
Opportunities
OPP-4471 · $28krecorded
Stage · Negotiationrecordedhidden
Contacts
Primary · J. Riverarecorded
Email · verifiedfilled in
Renewal case · missing from your data, filled in with the answer keyfilled in
update OPP-4471update OPP-4471Tasks 4472 · 4473Same 40 tasks40 tasks · 3 modelsAgent missed itSales ops agent
VerdictVerdictEval · 3 tasksReadiness report
Environment ready
The working copy is built and the answer key is minted from recorded values. Nothing has run yet, so there is no verdict. Send a task to grade it.
Task completed against the working copy. Matched the known answer.
passOn recordTask 4471 · matched the known answerpass
Task 4472 · the agent's answer did not matchfailpass
Task 4473 · a required field was not recordedfail
Points to the agent, not your environment. The twin served recorded values; the claim did not match.Points to the environment, not your agent. The working copy refused to make something up. Record the field, the test gets stronger.expected value · recorded · agent's answer · mismatchrequired field · not recorded · no ground truth
Sales ops agent · Readiness report
Model A
88%
Model B
84%
Model C
71%
Ready under budgetOn record
Illustrative simulation.
1 · Build
CRMOrdersPaymentsGift-card casefilled in
The tools and records your agent works with are assembled. Every value labelled: recorded, or filled in with the answer key.
2 · Run
Task 4471 · refund $28Refund agentRefund posted · $28.00pass
The agent reads the records field by field and acts. A grader stamps the verdict against the known answer.
3 · Eval
Task 4472failAgent missed itTask 4473failEnvironment couldn't answer
Two failures, two causes. Every verdict points to the agent, or to the environment. That difference is everything.
4 · Optimize
v1 61%v2 74%v3 88%
Compare two illustrative versions on the same tasks. The example scores show how the comparison works, not measured customer results.
5 · On record
Model A88%Model B84%Model C71%Ready under budgetOn record
Review the example results and their supporting evidence. Repeat the evaluation as the agent changes.
Evaluations
Evaluate outputs and actions.
Define what successful work requires. Check the result, inspect how the agent got there, and compare versions.
$40≠$28The agent returned $40.The answer key says $28.Every grader below catches the gap.
One answer, graded three ways
Under testRefund a cancelled insurance policy
Agent returned$40.00
Answer key$28.00
The same input, sent to each grader type on the right. Figures shown are illustrative.
expected $28.00returned $40.00
Graded 5 times · identical every run
FAILFAILFAILFAILFAIL
A verdict you can reproduce and defend. No model in the loop, nothing to drift or game.
expected $28.00returned $40.00
Same answer, 5 times · the verdict wobbles
FAILPASSFAILFAILPASS
Let the model check itself and the answer slips through half the time. Nothing to reproduce, nothing to trust.
Check work against your requirements. Inspect the result and the evidence behind each evaluation.
Bring your ownPrivate & fine-tuned modelsBenchmarks as CSV or JSONAuto-generated from your docs
Compare models head-to-headRe-run to compare versions
Synthetic Data
Generate the cases your data is missing.
Create evaluation datasets on demand, each example carrying a known ground-truth answer. Dial the difficulty, cover the situations your production data never captured, and reuse them across environments and evaluations.
Scenario→Case + answer keyDescribe a situation; get a labelled example with known ground truth. Figures illustrative.
One scenario, generated with its answer keyNo real data required
Scenario definitionCustomer disputes a refund past the prorated limit
DifficultyHard
VerticalInsurance
RecordsSynthetic, labelled
You describe the situation; difficulty and coverage are configurable. Figures shown are illustrative.
Prorated limit encoded in the ground truth
Cancellation date drives the calculation
An edge case your production logs never captured
No real or sensitive data required. Every generated example is reproducible and carries its own answer key.
No real data required, reproducible. Every generated example carries known ground truth and reruns to the same result.
What you getKnown ground-truth labelsConfigurable difficultyCoverage across industry verticals
Where it fitsScenario-defined traces for CI/CDFeeds environments & evaluations
The difference on record
Where the failure happens is everything.
Same agent, same mistake. See where it surfaces.
Before evaluation
You ship the agent. Its flaw ships with it.
The errorRefunds $40 on a $28 policy
The cost$12 a refund, thousands a quarter
The trustA customer flags it, in production
An incident and a support queue. Unverified.
After evaluation
You test the agent. Review the result before release.
The errorFlagged $40 vs the $28 answer key
The cost$0 reaches an account, ever
The trustThe record flags it, before ship
A test result, with the evidence attached.On record
Independence
See whether your changes helped.
After changing an agent's instructions, tools, or model, give both versions the same work.
01
Your business requirements
Define the outcomes and constraints the agent needs to meet.
02
Comparable test conditions
Give agent versions the same tasks and starting records to compare their results.
03
Inspect outputs and actions
Check what the agent did and whether the resulting work meets the requirements.
04
On record
Review the task result and the steps behind it before deciding what to change.
Keep testing as your agent evolves.
About LayerLens
Agent Readiness Platform.
LayerLens connects agent environments, evaluations, and synthetic data. Test the work your business requires and use the evidence to guide the next change.
Your business requirementsDefine the outcomes and constraints the agent needs to meet.
3Connected capabilities
DozensProduction replica environments
1Continuous evaluation loop
On recordInspect results and evidence
We evaluate the models your agents are built on
What your team gains
Built for the people on the hook.
From the blog
What we're learning, in the open.
Field notes on agent readiness, evaluation craft, and the mechanisms that make evidence trustworthy.
Build a benchmark from your agent's own work
LLM Evaluation Metrics: The Only 7 You Need- LLM Benchmarks Explained: What the Scores Mean
Bring your workflow to LayerLens.
See how LayerLens can help you test and improve an agent for your business.
Compare performance and evaluation cost.
$2.10 spentcap $5.00
Example evaluation budget across 40 tasks and 3 models.illustrative