Stop being the integration layer.
Most of the queue is lookups, access requests and copying state between systems never designed to talk to each other. Exemplary AI connects to 170+ of them through MCP, or the one you wrote yourself, and does the checking with the permissions you granted, on your own cluster.
“the export job failed again”
listRuns()1 failed · 02:14searchIssues()open since TuelistDeploys()config change 01:50tailErrors()42 matchingcheckAccess()unchanged- Message posted
- You go looking
- Tool calls fan out
- Results return
- Correlated
- One cause
Almost none of it is engineering.
The queue is not hard. It is scattered across systems that never agreed on a shared idea of a user, a service or an environment.
Everything joins up through a person
Provisioning a joiner, syncing an owner change, reconciling a licence count against the directory. Each one is a person retyping a fact into a second system because nothing in the stack carries it across.
The helpdesk queue is mostly lookups
Access requests, environment status, 'is it just me'. Every one is answerable from systems you already run, by someone who knows which system to open.
Context lives in five tabs
Diagnosing anything means logs here, the ticket there, the deploy history somewhere else — and one person holding all three in their head at once.
Most agent tooling wants your credentials in its cloud
Which ends the conversation for anything touching production, and is why so many internal AI pilots never leave the sandbox they started in.
Wired into the stack, not bolted beside it.
An agent is only as useful as what it can reach. This is the part of the platform that does the reaching.
One agent, connected to the systems you already run.
170+ integrations through MCP servers, native connectors and third-party apps. When the thing you need is internal, register your own MCP server and it becomes available to every agent. An integration here is not a logo; it is a set of typed tools with permissions you grant individually.
- 170+ apps across developer tooling, service desk, project tracking, cloud and identity, communication and storage
- Custom MCP servers for internal systems, usable across every agent
- Per-agent tool permissions — an agent gets the tools you grant it, and no others
An integration is a set of typed tools — granted per agent, not per workspace.
Internal helpdesk
Access, status and how-do-I questions answered from your own runbooks, in Slack, with the runbook attached.
Sandboxed execution
Code-execution tools run in a sandbox with the permissions you granted — seeded and managed by the platform, not by the agent.
Scheduled automations
Recurring checks that run on their own and report only when something actually needs a person, with full run history.
Someone posts “the export job failed again” at 11:20.
The old version of this is twenty minutes of tab-switching before anyone knows what is wrong. Here, four of the five steps happen before a person reads the thread.
The agent reads the message as a request, not a string.
Which job, which environment, and whether 'again' refers to something already in the ticket history — established before anything is queried.
It checks the systems the runbook would have told you to check.
The scheduler, the ticket tracker and the deploy history, through the integrations the workspace has already connected, with the tool permissions that agent was granted.
It correlates instead of reporting.
A failed run, a ticket open since Tuesday and a config change deployed at 01:50 are three facts sitting in three systems. Putting them together is the work, and the answer is that they are one event.
It answers in the thread, with what it checked.
The finding, the evidence, and the systems it looked at. Anyone reading can verify the chain rather than trusting the conclusion.
A person decides what happens next.
Rolling back, restarting or escalating stays with the engineer on call. The agent removed the twenty minutes of looking; it does not touch production on its own.
Nothing in those steps is specific to a failed job. An access request runs them against the directory and the service desk. A standing check runs them at six in the morning with nobody in the thread. What changes is which systems get asked, and who the answer goes back to.
“can the new analyst get into the billing dashboard before Thursday?”
- Identity
listGroupsnot in Billing-Read - Service desk
findPolicyowner approval required - Licence register
countSeats3 unassigned
One reply: the group they are missing, the owner who approves it, and the seat already paid for.
Each call used a tool this agent was explicitly granted. The approval is still a person.
Thirteen checks ran before the shift started. Ten finished and said nothing.
- Certificates
listExpiring2 inside 14 days - Backups
verifyRestoreTuesday test incomplete - Access review
listStaleGrants1 past its review date
Three things need a person this morning. All three were on the screen before the first message arrived.
Nobody requested this run. It went out on schedule with the tools this agent was granted, and the run history is kept.
The queue stops setting the agenda.
A platform team's week is shaped by what other people asked for, in the order they asked it. When the lookups answer themselves and the standing checks run on their own, what is left in the queue is the part that actually needed an engineer.
The MCP server you wrote for one system is a tool any agent can be granted. You stop being the request path.
The runbook you have recited for two years answers where the question was asked, with the runbook attached.
The checks nobody had a morning for run at six, and speak only when something needs a person.
Your credentials, your cluster, your call.
An agent with access to internal systems is a security decision before it is a productivity one. This is what makes that decision approvable.
- The platform runs inside your infrastructure — bare-metal, private cloud or fully air-gapped.
- Organisation credentials live in your deployment and override platform defaults; secrets stay masked in the interface.
- Tool access is granted per agent. An agent holds the permissions you gave it and cannot widen its own scope.
- Sandbox tools execute in a managed sandbox, seeded by the platform rather than assembled by the agent at runtime.
- Every invocation is metered and logged — which agent, which tool, which user, and when.
Model choice is yours too: hosted providers, or local models running entirely on your own hardware.
The connective tissue, and what governs it.
The parts of the platform an operations team actually configures — and the parts that keep the rest of the organisation out of them.
- Connectivity
- 170+ integrations via MCP, native and third-party
- Extensibility
- Register your own MCP server for internal systems
- Execution
- Native tools and sandboxed code-execution tools
- Scheduling
- Automations with run history and background tasks
- Secrets
- Organisation credentials that override platform defaults
- Access
- Per-agent tool permissions; IAM roles and policies
- Models
- Hosted providers or local models on your own hardware
- Metering
- Per-invocation usage records, plus queue and performance monitoring
What platform and IT teams ask first.
See Exemplary AI in action
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