Instrumentation gap
No Instrumentation Point
Traditional apps emit telemetry from known code paths. AI agents call arbitrary programs and tools - there is no reliable hook to attach to.
NewAgent Runtime Security + Observability
AI Agent Monitoring
At the Kernel Layer
Traditional instrumentation misses what agents do in subprocesses and tools. Metoro uses eBPF to capture every request, action, and anomaly - with no code changes.
Free trial available · No code instrumentation required
Trusted by hundreds of the best at
Porter
Remy Security
Porter
Remy Security
DocioHealth
Porter
The Problem
Monitoring AI Agents Is
a New Runtime Problem.
The Solution
Observe Once
At the Kernel.
eBPF instrumentation at the kernel layer captures what every agent process is doing - regardless of language, framework, or toolchain. Instead of chasing instrumentation across scripts, you monitor the common syscall boundary.
- ✓One capture point covers all agent runtimes
- ✓No SDK hooks, no sidecars, no code changes
- ✓Works with Python, Ruby, Node, Bash, curl - anything
- ✓Every outbound call passes the kernel - nothing escapes
Capabilities
What You Can Do With
AI Agent Monitoring.
<5m
install time
0
code changes
1000s
nodes in production
Customer feedback
What teams are saying.
Metoro has made visibility into our Kubernetes environment effortless with on-demand event analysis and AI-driven root-cause investigations. Nothing is hidden anymore.
Metoro absolutely slaps, so good ❤️
Detection, investigation, and the fix PR - all before I finished reading the page. It's the first AI SRE that's actually earned its name.
Metoro has been a huge boon to our observability ecosystem; saving us time and effort getting the information we care about most out of our clusters. The only thing cooler than the tool has been the people behind it.
It found exactly what I was looking for in the logs. Amazing.
We used to spend an hour digging through dashboards when something broke. Now Metoro figures it out in minutes - our on-call engineers love it.
AI root cause analysis is just amazing. Helps us save a ton of time.
We installed Metoro, and it just worked.
I'm literally able to look up at a Slack notification from Metoro whilst having noodles, tap the link, access the Metoro dashboard, see what customers on Porter Cloud are doing and take a call in real-time. For me, that's the best thing ever.
In the last week, we've detected and blocked 10 malicious agents running on our infrastructure. Without Metoro, they would still likely be running.
Metoro made it incredibly simple for us to not just observe and trace logs, but also to dive into AI-driven investigations effortlessly - turning complex Kubernetes monitoring into a smooth, intuitive experience.
Anyone running user agents on their infrastructure needs a solution like Metoro. It's just a case of when, not if a malicious agent will be running.
FAQ
Frequently Asked Questions
Everything about AI Agent Monitoring.
What is AI agent monitoring?
AI agent monitoring is runtime visibility for autonomous agents: what prompts they receive, which tools they call, which network requests they make, what files or subprocesses they touch, and whether their behavior changes in risky ways. Metoro captures that activity from the operating system layer so teams can audit agents without adding SDK code to every framework.
How is AI agent monitoring different from LLM observability?
LLM observability usually focuses on model calls, prompts, completions, latency, tokens, and cost. AI agent monitoring also covers the agent runtime around the model: tool use, shell commands, outbound API calls, database access, Kubernetes activity, and subprocess behavior that traditional LLM tracing can miss.
Why use eBPF for AI agent monitoring?
eBPF lets Metoro observe agent behavior from the kernel without changing application code. That matters for AI agents because important actions often happen outside the model call, such as a spawned process, HTTP request, package install, or tool invocation. Kernel-level monitoring gives a consistent audit trail across languages and agent frameworks.
Can Metoro monitor AI agents running on Kubernetes?
Yes. Metoro is built for Kubernetes environments and can monitor AI agents running in pods, jobs, services, and worker processes. It correlates runtime behavior with Kubernetes metadata so teams can see which workload, namespace, service, or deployment produced a risky action.
Which AI agent frameworks does Metoro support?
Because Metoro observes runtime behavior with eBPF, it is not tied to one agent framework. It can monitor agents built with common Python, Node.js, and other runtimes, including agents that use LangChain, LlamaIndex, custom tool loops, browser automation, shell tools, or internal orchestration code.
Does AI agent monitoring require code changes?
No. Metoro does not require agent SDK instrumentation to capture runtime activity. You install Metoro in the Kubernetes environment, and it observes requests, process activity, and telemetry from the infrastructure layer.
Can Metoro detect risky or malicious AI agent behavior?
Metoro can surface suspicious runtime behavior such as unexpected outbound requests, unusual tool use, anomalous subprocess activity, and changes in traffic patterns. It gives security and platform teams the evidence they need to investigate agent behavior quickly.
How does Metoro help with AI agent audit logs?
Metoro creates an operational audit trail for agent activity by capturing runtime events and connecting them to services, pods, requests, and external dependencies. This helps teams answer what an agent did, when it happened, and which infrastructure was involved.
What scale of traffic does Metoro support?
Metoro is running in production on clusters with thousands of nodes and billions of requests per day.
Does Metoro run on-premises?
Yes. Metoro can be deployed on-premises or in any cloud provider. As long as Kubernetes is available, Metoro can be deployed.
What levels of the network stack does Metoro observe?
Metoro primarily observes L4 and L7 protocols, including HTTP, HTTPS, gRPC, and more. For AI agent monitoring, that means teams can see the agent runtime activity around model calls, APIs, services, and external tools.
Can Metoro monitor agent tools and external API calls?
Yes. Metoro can capture outbound requests and correlate them with the workload that made them. This is useful for monitoring agents that call internal APIs, third-party services, vector databases, browser tools, code execution tools, or other external systems.
Does Metoro offer pilots?
Yes - a free 30-day pilot with support from a dedicated engineer is generally available for evaluation.
How much does Metoro cost?
There is a free tier for hobbyists. Cloud pricing starts at $20/node/month. On-premises is priced based on support level.