We believe autonomous agents deserve the same observability as traditional software
AgentTrace Monitor was born from a painfully simple question: when an AI agent fails in production, how do you figure out why?
The problem we set out to solve
As engineering teams shipped more autonomous agents — from customer-support bots to multi-step research assistants to agentic coding tools — a pattern emerged. Traditional APM, logging, and monitoring stacks were not built for the unique failure modes of LLM-powered agents.
Agents loop. They hallucinate tool calls. They burn through token budgets in minutes. They violate business policies in ways that are invisible until a customer complains. And when something goes wrong, the only recourse is hours of manual log parsing across fragmented systems.
We founded AgentTrace Monitor to close this observability gap. Our platform was purpose-built for the agent era: structured tracing that understands LLM reasoning, cost governance that prevents runaway spend, and compliance guards that enforce policies at the observation layer.
Today, AgentTrace is used by ML engineers, platform teams, and AI product managers to ship agents to production with confidence — and to debug them in seconds when things go sideways.
Make every AI agent observable, accountable, and safe
We are building the standard observability layer for autonomous AI systems — so engineering teams can innovate faster without sacrificing reliability, cost control, or user trust.
What we stand for
Radical Transparency
Autonomous systems must be inspectable. We build tools that make every agent decision visible, auditable, and reproducible — because trust requires evidence.
Safety by Default
Guardrails should not be afterthoughts. AgentTrace bakes compliance checks, cost limits, and policy enforcement into the observation layer so safety ships with every deployment.
Developer-First Speed
Observability tools should accelerate teams, not slow them down. We obsess over sub-second trace queries, one-click replays, and zero-config integrations.
Customer-Driven Roadmap
Our roadmap is shaped by the teams running agents in production. Every feature we ship solves a real problem reported by real engineers in real incidents.
Join us in making AI agents trustworthy
Get early access and help shape the future of agent observability.