Evaluation Infrastructure
for Production AI Agents
The unified platform for testing, scoring, red-teaming, and continuously monitoring AI agents. Prevent regressions, prove compliance, and ship with confidence.
Trusted by teams building on
From raw agent to production-ready,
every step is evaluated.
Scroll to move data through the evaluation pipeline — each stage feeds the next.
Six pillars of rigorous
agent evaluation.
Every feature is designed with a scientist's mindset — measurable, reproducible, statistically valid.
Trace & Trajectory Explorer
Visual step-by-step replay of every tool call, retrieval, and reasoning step. Diff two runs side-by-side and find the exact failure point.
Multi-Tier Scoring Engine
Rule-based, LLM-as-a-judge, embedding-distance, and statistical scorers running in parallel on every trace — calibrated, bias-mitigated.
Regression Gates / CI-CD
Block or allow deployments automatically based on evaluation results. One YAML config gates your entire release pipeline.
Guardrail & Safety Testing
Purpose-built red-teaming against prompt injection, jailbreaks, bias, and data-leak attacks. OWASP LLM Top-10 aligned.
Dataset Builder
Assemble golden, synthetic, production-sampled, and adversarial test sets. Version-locked so results are always reproducible.
Simulation Engine
Persona-driven simulated users run full multi-turn conversations. Test edge cases at scale without real users.
See exactly why your
agent got it wrong.
Every step of a trajectory is captured — planning, retrieval, tool calls, reasoning, final response. Compare two runs side-by-side. Find the regression in seconds.
- Waterfall timeline of all tool calls with cost & latency
- Golden-set diff to detect semantic regressions
- One-click escalation to Human Review
- Real-time streaming from production traffic
Rigorous AI drives
measurable improvement.
Adversarial testing built
for LLM attack surfaces.
Every deployment is automatically probed against the OWASP LLM Top-10. Successful attacks are automatically converted into regression tests so the same vulnerability never ships twice.
- ✦ OWASP LLM Top-10 attack taxonomy
- ✦ Direct & indirect prompt injection
- ✦ PII extraction, jailbreak, and data poisoning probes
- ✦ Automatic regression test creation from successful attacks
Every tool you need to evaluate,
monitor, and prove compliance.
Voice Evaluation
Audio-native testing — ASR, TTS latency, barge-in detection, and concurrent-call load testing.
Prompt Playground
Draft, diff, test, and promote system-prompt changes safely. Prompts are code — version-controlled.
Human Review Workspace
Expert-in-the-loop annotation. Route low-confidence cases to reviewers, track inter-rater agreement.
Benchmark Leaderboard
Compare agent versions, prompt versions, and models on a statistically rigorous cost-quality frontier.
Cost & Latency Profiler
Break down token cost and latency per step. Find the exact tool call bleeding your budget.
Compliance & Audit Trail
Immutable, tamper-evident ledger. One-click EU AI Act / NIST AI RMF evidence reports.
Connector Hub
Ingest and version-lock every knowledge base, schema, and tool definition the agent depends on.
Dashboards & Alerting
Real-time anomaly detection and threshold alerts. Slack / PagerDuty routing by severity level.
SDK & API
Two lines of Python to start tracing. SDKs for Python and TypeScript with auto-instrumentation.
Regulatory-grade audit evidence,
generated automatically.
Every evaluation run, human review decision, and prompt promotion is written to an immutable, hash-chained audit ledger. One-click reports for regulators.
EU AI Act
Art. 9, 11, 12, 14, 15Automatic technical logs, human oversight records, risk management evidence.
NIST AI RMF
Govern · Map · Measure · ManageFull lifecycle coverage across all four RMF functions with evidence bundles.
GDPR
Art. 5, 22, 30PII scrubbing logs, automated decision records, processing activity reports.
ISO/IEC 42001
Annex AAI management system controls mapped to evaluation evidence.
All your tools,
one seamless workflow.
Two lines of code to start tracing. Evaluation data flows into the tools your team already uses.
import agy_eval
from openai import OpenAI
# 1 — Initialize
agy_eval.init(api_key="ev_abc123", project_id=5)
# 2 — Instrument (captures every call automatically)
agy_eval.instrument("openai")
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Refund order 1234"}]
)
# ↑ This call is automatically traced, scored, and stored.Create your next AI agent
with confidence.
Stop guessing whether your prompts improved the system. Rigorous AI gives your team the evidence to ship AI faster and safer.
No credit card required. SOC 2 Type II in progress.