Skip to main content
Insights is Netra’s automated quality and behavior monitoring layer for AI applications. It learns what normal looks like for your agent from real production traffic, then continuously surfaces changes - new user intents, shifts in tool usage, latency creep, rising error rates.

​
How Insights Works

Insights operates on the trace data flowing through Netra. Once your application is instrumented, Insights runs in the background to:
  1. Discover and classify user intents from real traffic
  2. Build rolling baselines of normal behavior per intent
  3. Detect drift across cost, latency, step count, error rate, tool usage, and output length
  4. Generate a brief summarizing significant changes after each observation run
No rule configuration required. Insights ships with calibrated defaults for all metrics.

​
Rolling Baselines

Insights builds a baseline automatically from the last 30 days of traffic, refreshed daily. The baseline holds per-intent statistics - average cost, latency, step count, error rate, output length, and tool distribution - against which every drift observation is measured. No manual baseline tagging is required.

​
Brief

After each observation run, Insights generates a short summary of the most significant drifts and new intents detected during that cycle. The brief gives you a digest you can scan in seconds rather than a wall of metrics.

​
Setting Up Insights

Insights runs on your trace data. Instrument your application with the Netra SDK and Insights activates automatically once traces start flowing.
1

Install the SDK

pip install netra-sdk
2

Initialize Netra

from netra import Netra
from netra.instrumentation.instruments import InstrumentSet

Netra.init(
    app_name="my-ai-agent",
    environment="production",
    headers=f"x-api-key={os.getenv('NETRA_API_KEY')}",
    instruments={InstrumentSet.OPENAI, InstrumentSet.ANTHROPIC},
)
3

Instrument your workflows

Use Netra’s decorators so Insights can compute per-intent metrics at the workflow, agent, and task level.
from netra.decorators import workflow, agent, task

@agent
class SupportAgent:
    @workflow
    def handle_query(self, query: str) -> str:
        ...

    @task
    def fetch_account(self, user_id: str):
        ...
4

Let Insights learn

Once traces start flowing, Insights handles the rest automatically:
  • After ~500 traces, intent clusters are bootstrapped and classification begins for every new trace
  • A rolling 30-day baseline is built and refreshed daily
  • Drift observations run on daily, weekly, and monthly windows
  • A brief is generated after each observation cycle
Insights requires no additional configuration beyond standard SDK instrumentation. All analysis runs against trace data already captured by Netra.

​
Learn More

Intents

Discover and track what your users are actually asking

Drifts

Detect behavior changes before users notice them

Traces Overview

Understand how Netra captures the trace data that powers Insights

Decorators

Instrument workflows, agents, and tasks for per-intent metrics
Last modified on June 8, 2026