Now in Early Access

The Reliability Layer
for AI Agents

Monitor, protect, and debug your AI agent pipelines in production. Circuit breakers, blast radius containment, rollback & replay — built for the agentic era.

agentsentinelai.com/dashboard

📊 Overview

🔀 Workflows

🔗 Traces

🚨 Incidents

💥 Blast Radius

🛡️ Reliability

⏮️ Replay

🎯 SLOs

97.3%

Success Rate

23 ok / 1 failed

Open Incidents

3 critical

Active Workflows

6 active agents

1/4

Circuit Breakers

⚠ 1 OPEN

The Problem

AI agents fail in ways
you can't see coming

Traditional APM tools were built for deterministic software. AI agents are non-deterministic, multi-step, and chain together — they need a completely different reliability layer.

🔗

Silent cascading failures

One agent fails quietly. Three downstream agents never run. Your users get a broken experience with no error in your logs.

🔄

Infinite agent loops

An agent calls the same tool 50 times in a loop. You burn $200 in API costs before anyone notices. No circuit breaker to stop it.

🕵️

No replay capability

A long-running agent fails at step 47 of 50. You have to restart from scratch. No checkpoints, no rollback, no way to debug the exact failure.

Features

Everything you need to run
agents in production

🔀

Multi-Agent Orchestration Tracing

Track every handoff across agent pipelines. See the full DAG of which agents called which, where the chain broke, and why.

Cascading failure detection

💥

Blast Radius Containment

Before a failure spreads: "If the orchestrator fails, it affects 3 downstream agents, 47 users, and 12 active workflows." Contain it instantly.

Dependency graph analysis

⚡

Circuit Breakers

Auto-stop routing to a failing agent after N failures. Auto-recover after a configurable timeout. No human intervention needed.

Auto-recovery

⏮️

Rollback & Replay

Every agent step is checkpointed. Replay from any exact point with modified inputs — without re-running the full workflow from scratch.

State snapshots

🎯

Error Budget SLOs

Not just "is it broken" — "at this burn rate, you'll exhaust your reliability budget in 4 hours." Proactive alerts before SLOs breach.

Burn rate alerts

📬

Dead Letter Queue

Failed tasks don't disappear. They queue up with full context — error, retry count, state snapshot — and you retry them with one click.

Zero task loss

How it works

Up and running in minutes

Wrap your agent

Add 3 lines of Python to your existing agent code using our SDK

Traces flow in

Every LLM call, tool use, and agent handoff is captured automatically

Failures detected

Loops, cascades, silent errors, and SLO breaches are caught in real-time

Replay & fix

Replay any failed run from any checkpoint with modified inputs

Python SDK — 3 lines to instrument your agent
# Before
response = openai.chat.completions.create(...)

# After — full observability, circuit breakers, replay
from agent_sentinel import AgentTracer
tracer = AgentTracer(endpoint="https://agentsentinelai.com/api/agent/spans")

with tracer.trace("my-agent", session_id=sid) as trace:
    with trace.span("llm_call", model="gpt-5.2") as span:
        response = openai.chat.completions.create(...)
        span.set_tokens(prompt=response.usage.prompt_tokens, ...)

The Reliability Layerfor AI Agents

The Reliability Layer
for AI Agents