AI Agent Monitoring

AI agent monitoring means watching what your agents decide in production — not just whether they responded, but whether each decision was sound, policy-compliant, and safe. Histeeria provides real-time AI agent monitoring with zero impact on agent latency. Connect once via SDK or API; every turn flows to your command center automatically.

Why production agent monitoring matters

Agents in staging behave differently than agents under real user pressure:

Adversarial prompts and edge cases spike in production
Model updates silently change behavior
Tool calls fail or overreach without obvious errors
Teams discover mistakes from user complaints — too late

Monitoring closes the loop: you see every decision as it happens and catch regressions before users do.

What Histeeria monitors

Signal	Captured
Input	Prompts, messages, RAG context
Output	Agent responses and action summaries
Session	Conversation and workflow run IDs
Metadata	Tags, user IDs, model version, escalation flags
Tokens	Optional input/output token counts
Multi-step traces	Intermediate steps in complex agents

See Monitoring in the app and Decisions and evaluation for the data model.

Real-time vs batch

Real-time ingest — decisions appear in Monitoring within seconds
Async evaluation — judgment scores follow via the evaluation engine
Alerts — incidents land in Inbox when thresholds breach

How to set up agent monitoring

Create agent profile

Define role and policy in Agent profiles.

Generate API key

Link key to profile in API keys.

Add SDK

One observe() call after each agent turn. Quickstart.

Watch Command center

Metrics and decision stream in Command center.

Code example

from histeeria import Histeeria

h = Histeeria()  # HISTEERIA_API_KEY from env

response = agent.run(user_message)

h.observe(
    input=user_message,
    output=response,
    agent_id="support-bot",
    session_id=session_id,
    domain="customer_support",
)