Protect Customer Service AI Agents
From the Messages They Receive.
Adopt autonomous AI agents for customer care, safely
Customer-service teams want to adopt autonomous agents. What holds them back is the risk of an untrusted message manipulating or hijacking the agent. Metomic inspects every inbound message first, so it can't.Metomic inspects every inbound message before the agent acts on it, so adoption doesn't mean exposing it to manipulation.
Zendesk · Salesforce · HubSpot · ServiceNow · SOC 2 Type II · AICPA
Metomic inspects every inbound message before a customer-support agent acts on it, catching PII and prompt injection the moment it arrives.
What makes it safe to turn these agents on
Zendesk AI Agent, Agentforce, HubSpot's bot, and Now Assist can all take messages straight from customers. Metomic clears that message first, so rolling the agent out doesn't mean giving up visibility into what it's being sent.
Works with the platform you've already chosen
Metomic connects directly to Zendesk, Salesforce, HubSpot, and ServiceNow, wherever your customer-service agent already runs. There's nothing new to install and nothing to change about how the agent itself is set up.
- No agent-side install, no proxy, no network change
- Nothing for IT to roll out before the agent can go live
- Fits alongside the security tools you already run
Reads the message, not just who sent it
Metomic checks what's actually inside the message for sensitive data and for attempts to manipulate the agent. A customer typing a card number into a support chat, or trying to talk the agent into ignoring its own instructions, gets caught before the agent ever sees it.
- Checks the whole message, not just where it came from
- Catches attempts to manipulate the agent, not only sensitive data
- Uses the same policy your security team already trusts elsewhere
Decides, in the moment, what the agent gets to see
Metomic allows the message through, cleans it up first, or holds it for a person to look at, whichever fits. The customer never sees a delay or a dropped conversation; the platform just picks up where it left off.
- Allow, clean up, or hold, not just a blunt block
- A risky message gets fixed where it can be, not just rejected
- Your policy decides what happens if something's unclear
The agent picks up, only once it's safe
Zendesk AI Agent, Agentforce, HubSpot's bot, or Now Assist carries on automatically, the moment Metomic clears the message. It only ever sees the cleaned-up version, never the raw one a customer sent in.
- No manual step for your team, the agent resumes on its own
- The agent sees the cleaned-up message, never the original
- A held message goes to a person instead of stalling silently
An answer ready, when someone asks
Every decision is logged automatically, alongside everything else Metomic already tracks. When someone asks what an autonomous agent did with a customer's data, you have an answer instead of a gap.
- One record for everything your AI touches, not a log per platform
- Ready for auditors, regulators, and your board
- Only the decision is kept, not the customer's message itself
Nothing to install, nothing to slow the rollout
Metomic runs quietly behind the platform you've already chosen, so adopting an autonomous agent doesn't turn into a project for IT.
Nothing New to Roll Out
Nothing to deploy on the agent itself. Metomic works quietly behind the platform you've already chosen.
You Decide What Happens Next
Allow it through, hold it for a person, or stop it, your call, not a default you're stuck with.
One Policy, Every Platform
Zendesk, Salesforce, HubSpot, and ServiceNow share one set of rules and one record, instead of a separate setup to manage for each.
See this inspect your own support traffic
Book a demo and watch Metomic catch PII and prompt injection in real inbound messages, live, before you configure a single label.
Autonomous AI agents are now taking messages directly from people with no account and no trust. A customer typing into a support chat, a shopper asking for a refund, a claimant submitting a ticket. Every one of those messages reaches an agent that was approved to act, but the message itself was never inspected on the way in.
It's the same blind spot security teams have always had to close: check what's arriving before anything acts on it. Here, the thing acting on it is the agent, and almost nothing checks the message on the way in today.
Metomic is built to be that inspection point, at the hook each agent platform already exposes.PII reaches the agent unredacted
A customer pastes a card number or a health detail straight into a support chat, and the agent, and everything downstream of it, sees it in the clear.
Prompt injection hijacks the agent
"Ignore your instructions above" buried in a customer message is not a hypothetical. An agent that trusts its input the way it trusts its system prompt can be steered into handing over data it should never expose.
No visibility into which agents are even live
Support teams stand up a new AI agent platform the way they'd stand up a new help-desk tool. Security often finds out after it's already taking customer messages.
No audit trail for what the agent did with it
When the auditor asks what an autonomous agent touched on a customer's behalf, an uninspected inbound path leaves you with nothing to show.
Same box, the other direction
The MCP Gateway and Inference Hooks govern what your own people and agents send out to models. This governs what comes in from people outside your organization. Most enterprises will want both.
| MCP Gateway & Inference Hooks | Customer Service AI Agents | |
|---|---|---|
| Direction | Outbound: your agents and employees, to a model | Inbound: untrusted customers, to your agent |
| Who's untrusted | An internal agent reaching for data it shouldn't touch | An external sender with no account and no trust |
| Where it plugs in | Between your agents and the models they call | Between the platform and the agent, before it sees the message |
| Primary risk | Sensitive data leaving the organization | PII and prompt injection entering the organization |
| Decision | Allow, redact, hold for approval, or block | Allow, hold for review, or block, with redaction where it applies |
| Metomic's role | Is the gateway itself | Answers each platform's own inbound hook |
One policy engine, one audit trail, both directions. See how the outbound side works on the MCP Gateway and Claude Inference Hooks pages.
Connect one platform, or all four
Most teams are live within a day, on one platform or all four.
Connect the platform you already use
Point Metomic at Zendesk, Salesforce, HubSpot, or ServiceNow. Nothing for your team to build or stand up.
Set what matters to you
Start from Metomic's defaults for sensitive data and manipulation attempts, or set your own. You decide what happens when something's unclear.
Watch it work, then turn it on
See what it catches before you change anything, then enforce at your pace. Every decision is logged from day one.
Source content is inspected in flight. Only decisions and findings are retained, for your audit trail.
Questions security teams ask about inbound AI agent traffic
What risk does this actually address?
Autonomous AI agents now take messages directly from people with no account and no trust: a customer in a support chat, a shopper asking for a refund. Nobody inspects what's inside that message before the agent reads it and acts, which means PII, card numbers, and prompt-injection attempts reach the agent exactly like any other instruction. Metomic inspects the message at the platform's own inbound hook, before the agent sees it.
How is this different from Shadow AI discovery?
Shadow AI discovery finds AI tools your own employees are using without approval. This is the opposite direction: it inspects messages arriving from people outside your organization, your customers, before a sanctioned, approved agent acts on them. Both matter; they cover different traffic.
How is this different from the MCP Gateway or Inference Hooks?
The MCP Gateway and Inference Hooks govern outbound traffic: what an internal agent or employee sends to a model. This governs the opposite direction: inbound messages from untrusted customers, arriving at a customer-service agent. Both run through the same Metomic policy engine and the same audit trail, just on different sides of the agent.
Which agent platforms does this cover?
Zendesk AI Agent, Salesforce Agentforce, HubSpot's chat bot, and ServiceNow's Now Assist today, connecting directly to each platform so there's nothing new for your team to build.
Does this slow down the conversation?
No. Inspection happens in the moment, before the agent is handed the message, and is built to fit inside the response time a conversation already expects. A customer shouldn't notice it happening at all.
What happens if a message can't be inspected in time?
You choose: hold it for a person to review, let it through, or block it. Nothing here defaults to letting a risky message slip through, or to stopping the agent for everyone, unless you decide that's what you want.
Does this replace Zendesk AI Agent, Agentforce, or Now Assist?
No. Metomic doesn't replace the platform's own agent, it sits in front of it. The agent keeps running exactly as configured; it just resumes only once Metomic has cleared the inbound message, the same handback pattern each platform already uses for its own inspection or verification steps.
What does Metomic see, and what does it keep?
Metomic inspects the inbound message content in flight: the text a customer submitted, at the point the platform's webhook already exposes it. Only the decision and any findings are retained for your audit trail; the underlying conversation stays in the platform of record.
Give your customer-service agent a decision it can trust
Book a demo and watch Metomic inspect real inbound traffic from your own agent platform, live, before you configure a single label.