How are people debugging multi-agent AI workflows in production?

Sentinel.AI — Reliability Infrastructure for AI Agents 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. 🤖 See Live Agent Demo 📊 View Dashboard agentsentinelai.com/dashboard 📊 Overview 🔀 Workflows 🔗 Traces 🚨 Incidents 💥 Blast Radius 🛡️ Reliability ⏮️ Replay 🎯 SLOs 97.3% Success Rate 23 ok / 1 failed 12 Open Incidents 3 critical 4 Active Workflows 6 active agents 1/4 Circuit Breakers ⚠ 1 OPEN Works with any LLM or agent framework OpenAI Anthropic LangChain AutoGen CrewAI Google Gemini Llama 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 1 Wrap your agent Add 3 lines of Python to your existing agent code using our SDK 2 Traces flow in Every LLM call, tool use, and agent handoff is captured automatically 3 Failures detected Loops, cascades, silent errors, and SLO breaches are caught in real-time 4 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, ...) 99.9% Uptime SLA <50ms Trace ingestion latency 6 Reliability primitives ∞ Agent frameworks supported Ready to see it live? Watch a real AI agent run in production — with every step traced, every failure caught, and full replay capability. 🤖 Live Agent Demo 📊 Open Dashboard Contact Us Get in touch Have questions, feedback, or want early access? We'd love to hear from you. Name Organization Email Query / Feedback Send Message →