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How AI stops answering questions and starts finishing the work
AI built for the support inbox answers from a document store, in one channel, for one department. That AI can quote the refund policy but can't issue the refund, can't see the account that's asking, and has nothing to say about the onboarding project or the partner request sitting next to it. MatrixFlows agents run on the foundation your whole customer operation already works in. They read the account, the ticket, the project, and the submission; they act through the systems you already run; and they operate inside rules you write in plain English. Every question they couldn't answer comes back as knowledge they can.
AI Agents — Build as many AI agents as you need. Every one of them runs everywhere.
A support agent for customers, an onboarding agent for each new account, a partner-qualification agent, an internal IT agent — each with its own instructions, persona, scope, and knowledge, and none of it requiring an engineer. Start from a template instead of a blank page. There's no cap on how many you build and no fee per agent, so you're never deciding which team gets AI this year. And one agent covers every surface at once — help center, customer portal, in-product, email, and voice. Edit it once and all of them change together, instead of maintaining a separate configuration for the chat bot and the voice bot.
Knowledge Grounding — Answers grounded in your records, not in a folder of PDFs.
Agents read your articles and policies, the content you've connected from SharePoint, Salesforce, Google Drive, and the web — and then the live records underneath: the customer's account, their open tickets, the project they're waiting on, the request they filed last week. Because every record is typed, the agent retrieves the exact right one and cites it, instead of guessing between look-alike documents. That's the gap a document store can't close — it can tell a customer what the policy says, never what's true about their account. Update the source once and every agent is current the same second.
Agent Actions — Agents don't tell the customer what to do. They do it.
Issue the refund, extend the trial, reassign the onboarding task, register the partner's deal, open the ticket in the system your team actually works in. Prebuilt tools query your records, create them, and update them; custom tools reach everything else through any private API, any of 1,000+ apps, or any MCP server you connect. MatrixFlows works both directions on MCP — your agents pull live data from other tools into one grounded answer, and an external AI can build and operate the workspace itself. Every call runs inside the permissions and guardrails you set.
AI Guardrails — Write the guardrails in plain English. The AI doesn't cross them.
Set the vocabulary, the length of an answer, and what the agent has to confirm before it answers — check the plan before quoting a price, ask which product before troubleshooting. Then set where it stops: nothing in the knowledge base, three replies without progress, a customer whose tone has turned. It hands to a person instead of guessing. Granular permissions decide which knowledge each agent can read and which tools it can use, your team reviews anything customer-facing before it ships, and every correction sharpens the next answer. Full auditability shows what the AI did and why.
Workflow Automations — Set the workflow automation once. It runs on every record after.
A trigger, conditions to narrow it, then the actions chain — assign the owner, notify the team, sync the systems, update the fields, or hand a step to an agent to draft the next move. Automations fire the moment a record changes, across teams and tools, and anything a customer will see waits for a person to approve it. This is the half of the work a chat product never touches: the routing, the escalating, the chasing, the updating your team does by hand between conversations. Set the rule once and it stops being done by hand.
The Enablement Loop — Every question it couldn't answer becomes an article it can.
MatrixFlows collects what the agents couldn't finish and the processes that stalled, groups them by theme, and drafts the missing knowledge from how your team actually resolved it. You review and publish, and the next customer finds it themselves. The loop spans agents and automations both — a question nobody could answer and a workflow that stalled at the same step are the same signal. Answer rates climb and escalations fall every week, and nobody reads a transcript to make it happen.
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