Features

Everything you need to keep AI on target

Drift detection, AI-powered log review and a setup small teams can finish in an afternoon.

Data drift detection

See exactly which data moved

EagleaAI profiles every feature, input and embedding cluster against your baseline, and tells you which ones changed and by how much.

  • Statistical drift tests — PSI, KL divergence and KS tests on structured and unstructured data
  • Embedding drift for text and images, so you catch new topics and new user behavior
  • Training-data audit — flags new data that is duplicated, mislabeled or out of distribution before it's fed to the model
Feature drift · last 30 days3 FEATURES
query_topic
0.41
user_locale
0.29
input_length
0.22
product_cat
0.08
channel
0.04
embedding_c7
0.33
Inference log review

An AI that reads every log so you don't have to

The secondary model reviews your agent's traces and outputs, scores them against the original benchmark behaviour and surfaces what actually matters.

  • Output regression scoring — compares live answers to golden answers from launch
  • Agent behaviour checks — loops, wrong tool calls, policy violations and hallucinations
  • Root-cause summaries written in plain English, linked to the exact log lines
Accuracy dropped 6.8% on refund questionsCRITICAL

New refund-policy tickets (+340% this week) fall outside the training distribution. 212 of 1,480 answers cite the old 30-day policy.

support-agent-v3 · cluster c7 · 2 min ago
Tool-call loop pattern emergingWARNING

4.1% of sessions call search_orders 5+ times in a row, up from 0.3% at baseline. Started after prompt v3.2 deploy.

support-agent-v3 · traces · 18 min ago
Suggested fixACTION

Add 120 refund-policy examples to the eval set and re-run the benchmark. Roll back prompt v3.2 to confirm the loop cause.

Built for small teams

Connect in minutes, not a quarter

Self-serve from day one. Drop in the SDK or forward your existing logs, upload a benchmark and you're monitored. No data science team required.

  • Works with any model — OpenAI, Anthropic, open-source or your own
  • Reads logs you already have — OpenTelemetry, LangSmith, S3, Postgres
  • Alerts where you work — Slack, email, PagerDuty or webhook
# pip install eagleaai
import eagleaai as ea

ea.init(api_key="ea_live_...")

# 1. lock in today's results as the baseline
ea.baseline(
  model="support-agent-v3",
  benchmark="evals/golden_set.jsonl",
  training_data="s3://data/train/",
)

# 2. wrap your agent; logs stream in the background
@ea.watch(alerts=["slack:#ml-alerts"])
def answer(query):
  return agent.run(query)