Cloud agents for operations are AI agents that run in the cloud, using the same SaaS tools your operations team uses, to do operations work. Because they live on shared infrastructure instead of on someone's laptop, they can multiply, scale, and run around the clock. That last part sounds like a technical footnote. It is actually the entire argument.
Local agents vs. cloud agents
The clearest way to understand a cloud agent is to look at the thing it is not. Tools like Claude Code are local agents. They are extraordinary, but they run on your machine, in a terminal, in a session you started. They do the work while you sit there. Close the laptop and the agent stops. One person, one machine, one task at a time.
A cloud agent runs somewhere else entirely: on infrastructure that is always on. Nobody has to have it open. It doesn't wait for a human to press go on each step. And because it isn't tied to a single machine, you can run one, or fifty, at the same time.
| Local / desktop agent | Caddi | |
|---|---|---|
| Where it runs | Your laptop, in a session you start | Cloud infrastructure, always on |
| Who drives it | A person sitting in front of it | A schedule or an event trigger |
| How many at once | One session, one task at a time | Many workflows in parallel |
| When it works | While you're at the keyboard | Nights, weekends, 24/7 |
| What it touches | Files and tools on that machine | The SaaS tools your team already runs |
There are already cloud agents. They're just for engineers.
Cloud agents aren't new. Vercel, GitHub, and others already run agents in the cloud that write, test, and ship code without a developer babysitting a terminal. They spin up on a pull request, do the work, and report back. That model is proven.
But every one of those agents does the work of the software engineering team. They live in repos, CI pipelines, and deploy platforms. The operations team, the people running intake, billing, collections, onboarding, and the shared inbox, never touches a repo. Their tools are Salesforce, Microsoft 365, the DMS, the practice-management system, and a dozen browser tabs.
Cloud agents for operations point the same deployment model at a different team. Instead of committing code, they open matters, reconcile invoices, triage an inbox, extract fields from a PDF, and move records between the systems your ops team lives in, in the cloud, on a schedule, at volume.
Why the cloud is the whole point
A local assistant that saves one person a few minutes is nice. A cloud agent changes the unit economics of an operation, and it does so through three properties a laptop can never have:
- It multiplies. The work of your ops team is repetitive and parallelizable. In the cloud you aren't limited to one agent doing one thing; you run the same workflow across hundreds of matters, invoices, or emails at once.
- It scales. Volume triples at quarter-end and the agents absorb it. There is no laptop to upgrade, no seat to buy per incremental task, no person to hire to cover the spike.
- It runs 24/7. The overnight batch, the weekend backlog, the invoice follow-up that should have gone out at 6am: work that used to wait for someone to arrive now happens while the team sleeps.
What cloud agents for operations actually run on
Operations work doesn't live in one app. It lives in the space between apps: pulling a field out of a PDF and into the CRM, taking an intake email and opening a matter, reconciling an invoice against the practice-management system. A cloud agent for operations has to reach across all of those tools the way a person would, because that is where the work is.
Runs on the tools your ops team already uses
Salesforce
Microsoft 365
iManage
Clio
DocuSign
NetDocuments
How Caddi builds cloud agents for operations
The hard part of an operations agent isn't running in the cloud; it's building one that does the work reliably when the inputs are messy and the process spans five systems. Caddi's approach isn't just recording a workflow into a script. Caddi is an agent that builds agents, and it learns from you the way a new hire would: you teach it over a screen share and a bit of back-and-forth conversation, and it figures out how the work should run. It turns what it learns into deterministic code that runs in the cloud, unattended, on a schedule, with an audit trail, so the agent behaves the same way every time while still using AI where judgment is genuinely needed.
That blend of deterministic execution with AI decision-making is what we call a hybrid agent, and it's why Caddi's cloud agents are both cheap to run and reliable enough to trust with production work. The cloud gives you scale; the hybrid architecture gives you the consistency to actually deploy that scale on real operations.
See cloud agents built for your operations
Explore the agents Caddi runs today, learn what an operations AI agent is and how your team manages one, or book a demo to watch one of your own back-office workflows built from a screen recording and put in the cloud.
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Frequently asked questions
What are cloud agents for operations?
Cloud agents for operations are AI agents that run in the cloud, using the same SaaS tools an operations team uses, to do operations work like intake, billing, email triage, and document movement. Because they run on always-on infrastructure rather than a person's laptop, they can multiply, scale, and run 24/7. The category is defined by where the agent runs: in the cloud on your ops tools, not locally on a single machine.
How are cloud agents different from local agents like Claude Code?
A local agent like Claude Code runs on your machine, in a session you start, doing work while you sit in front of it. Close the laptop and it stops. A cloud agent runs on shared, always-on infrastructure: it kicks off on a schedule or a trigger, runs many workflows in parallel, and keeps going overnight with nobody logged in. The difference isn't intelligence, it's the deployment model, and the deployment model is what unlocks scale.
Aren't there already cloud agents, like Vercel's?
Yes, but they do the work of the software engineering team. Cloud coding agents from Vercel, GitHub, and others write, test, and ship code without a developer babysitting a terminal. They live in repos and CI pipelines. Cloud agents for operations point the same always-on, parallel deployment model at a different team: instead of committing code, they open matters, reconcile invoices, triage inboxes, and move records across the SaaS tools the ops team already runs.
Why does running in the cloud matter for operations work?
Operations work is repetitive and parallelizable, which is exactly what the cloud is good at. Cloud deployment gives an agent three properties a laptop can't: it multiplies (run the same workflow across hundreds of items at once), it scales (absorb quarter-end volume without adding headcount), and it runs 24/7 (the overnight batch and weekend backlog get done while the team sleeps). It removes the human, the machine, and the one-task-at-a-time bottleneck from the loop.
How does Caddi build cloud agents for operations?
Caddi uses a record-to-code approach: an ops person screen-shares a task once, Caddi turns it into deterministic code, and that code runs in the cloud, unattended, on a schedule, with an audit trail. It behaves the same way every time while still using AI where judgment is needed. That blend of deterministic execution and AI decision-making is a hybrid agent, and it's why Caddi's cloud agents are both cheap to run and reliable enough for production operations work across 70+ integrations.