Enterprise AI

AI Agent Discovery

Inventory the AI agents running on your network.

CHALLENGES

You Can't Govern What You Can't See.

AI agents are proliferating enterprise networks – embedded in tools, spawned on demand by automation platforms, and deployed by employees to speed up workflows. They break traditional IT norms, in which applications are deliberately deployed and inventoried.

Even sanctioned agents can inadvertently violate security policies, trigger data leaks, or disrupt operations. They work fast and autonomously as they gather information from devices and applications.

Monitoring shadow agents challenges many network operations teams. Logs, metrics, and traces report changes in device state. But when agents choose their paths at runtime, evidence of their behavior lives in the traffic in between.

Before you can monitor agents on your network, you must discover and inventory them.

OUTCOMES

Packet-level evidence is the first line of defense against shadow AI.

Create an Agent Inventory

Discover every AI conversation from the traffic, rather than relying on change-records or deploying system agents on every device.

Reduce Operational Risk

Catch query floods and retry storms from well-intentioned agents before they degrade a production service.

Optimize LLM Token Costs

Verify actual LLM usage against vendor token reports by measuring consumption with data crossing the network.

Get Shadow AI Under Control

Each scan reports the agents that are new since the last one, so the team reviews a delta instead of the same list.

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SOLUTIONS

Explore how cPacket can help you solve this challenge.

TRADITIONAL SOLUTION

Traditional cPacket Solution

Packet-based observability enables IT organizations to inventory agents with network traffic data.

cVu packet brokers acquire traffic out of band across on-premises, virtual, cloud, and Kubernetes environments and deliver it to cStor capture appliances, which turn packets into session records (DNS queries, TLS server names, ports, and byte, packet, and session counts by direction). The cClear Control Center organizes those records by service and location.

The inventory and LLM usage data come from the tool endpoints that host actual queries – public model APIs and LLM endpoints. Passive packet acquisition eliminates the need for additional instrumentation or software at the endpoint – making this approach ideal for OT estates and appliances where agents can't be installed.

REQUIRED PRODUCTS

KEY FEATURES

Agentless Discovery

Packet-derived inventory needs no SDK, no code change, and no host software. It sees agents whether or not they cooperate with your instrumentation.

Inventory by Endpoint

Agents are identified by the model and tool endpoints they query, using DNS and TLS server names extracted by the cPacket appliances. Encrypted traffic stays encrypted.

Usage Measured on the Wire

Bytes, sessions, and direction per endpoint give an independent measure of LLM consumption, per service and per location.

AI SOLUTION

Right tool

cPacket AI Workflow

cPacket AI streamlines discovery and inventorying of AI agents.

cPacket AI brings the same session data into your own AI client through cPacket MCP servers and encoded skills, so operators can ask in plain language which hosts talk to model endpoints or which agent's query rate jumped.

The purpose-built skill pack classifies each flow on two axes: whether the caller is a person or an automated agent, and whether the destination is a model, a tool, or another agent. Classification uses flow metadata, destination names, ports, and behavioral signals rather than payload, so it works on encrypted traffic.

REQUIRED PRODUCTS

KEY FEATURES

Interaction Classification

Heuristics built on cPacket's L7 session data separate user, assistant, and agent traffic, and tell model calls apart from tool calls and agent-to-agent calls.

Memory Across Scans

The assistant holds the operator's decisions, so every scan reports the delta: the new agents, not the known ones.

Baseline and Deviation

Long-horizon baselines flag when an agent's volume, fan-out, or retry behavior moves outside its normal range.

CUSTOMER TESTIMONIALS

“We discovered 27 instances of a sanctioned agent querying an inventory system to gather context for another task. It took our system down – with no malice, no error, and nothing in a log that explained why.”

Lead Network Engineer

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Frequently Asked Questions

Get answers to common questions about network observability and cPacket solutions.

How do I find shadow AI agents?

Network engineers can find shadow AI agents by monitoring the record created as they cross the network – touching endpoints along the way.

Is decryption required to discover agent traffic?

With cPacket, you can discover encrypted agent traffic. Classification uses flow metadata, DNS and TLS server names, ports, and timing and volume behavior. Where traffic is unencrypted, the wire format can be decoded to corroborate.

Does this require software on every host?

Passive packet acquisition through cVu packet brokers eliminates the need to install software on each endpoint. That makes it usable in OT environments and on systems where host software isn't an option.

How is LLM usage measured without token counts?

cPacket can measure LLM usage via network data – bytes, sessions, and direction per model endpoint, broken out by service and location. It is an independent check on what the provider bills.

What is a rogue agent?

Operators, not inference models, determine what constitutes a rogue agent – assigning agents as allowed, blocked, or awaiting-decision. Anything observed that isn't allowed is rogue.

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