One system.
Two layers.
The Kernel is what every StaffOS agent runs on. Operations is where the work lands and your team takes over. Both halves ship together, which is why an agent never has to hand your business off to somebody else's software.
Operations
The surfaces your team works in. Conversations, tickets, pipeline, and calendar.
- Conversations
- Tickets
- CRM and pipeline
- Calendar
Agents run on the Kernel. Their work lands in Operations. One tenant, one permission model, one audit trail across both.
The Kernel
What every agent runs on. Reasoning, knowledge, learning, oversight, governance, and reliability.
- Orchestration
- Knowledge Engine
- Learning Loop
- Agent Twin
- Governance
- Reliability
The Kernel
The parts that make an agent reliable are built into the foundation, so every agent inherits them on the day you deploy it. There is nothing here for you to assemble, and nothing you can forget to add.
Orchestration
An agent that only answers questions is a search box. A StaffOS agent plans the job, runs the steps, calls the systems it needs, and checks the result before a word reaches your customer.
- Reads the whole thread before it acts, not the last message
- Calls your systems for the real answer instead of guessing at one
- Stops and asks a person when the next step is outside what it may decide
Knowledge Engine
Your documents, products, and policies become the working memory the agent answers from. It retrieves by meaning, so a customer asking in their own words still gets the paragraph that answers them.
- Upload documents and product lists and the agent uses them the same day
- Answers trace back to the source, so your team can check them
- Your material answers only your customers, and stays inside your workspace
Learning Loop
Every correction your team makes is a lesson. The agent proposes the change, you approve it, and the next conversation is better. Nobody has to go and rewrite a prompt.
- Learns from the replies your team edits and the answers they reject
- Brings changes to you as a proposal, never as an edit it made on its own
- Keeps every version, so you can see what changed and go back
Agent Twin
One brain, two views. The agent your customers meet is the same agent your team can talk to, so when you ask what happened this week the answer comes from the one that was there.
- Ask your own agent about its conversations, customers, and pipeline
- It prepares the work and a person approves anything that changes your data
- Same knowledge and same limits on both sides, so the two views never disagree
Governance
The reason a business can put an agent in front of its customers. Every action an agent can take is one you granted, bounded, and can read back afterwards.
- You choose what the agent does alone and what waits for a person to approve
- One audit trail for people and agents: who did what, when, and on what evidence
- Spend caps and rate limits per workspace, so a bad day costs a number you set
- Rehearse changes in a sandbox against real history before they go live
- Each workspace is walled off in the system itself, never in the agent instructions
Reliability
The part nobody asks about until the day it matters. Agents keep answering under real load, and when something does break the failure is visible instead of quiet.
- A failed step opens a ticket for a person rather than inventing a reply
- Timeouts and retries on every outside call, so one slow system is not an outage
- Tested against real conversations before a change reaches your customers
Operations
Where an agent's work becomes your team's work. These are the surfaces your people live in all day, and the agents write into the same ones.
Conversations
Every customer thread in one inbox, with the agent working alongside your team rather than in a separate tool.
- Your team can take over a thread at any point
- Reply from your own phone and the agent steps aside
- Full history, including everything the agent did
- One inbox across every channel you connect
Tickets
When an agent reaches the edge of what it should decide, it opens a ticket instead of guessing, and the conversation comes with it.
- Priority sets the clock, from one hour to one day
- Assign, add notes, and resolve as a team
- AI-assisted resolution drafts the answer for review
- Every ticket links back to the person it belongs to
CRM and pipeline
Every CRM fails the same way. Nobody fills it in. Here the agent is the one having the conversation, so the record fills itself while the customer talks. Built for hundreds of thousands of contacts per workspace.
- What a customer mentions becomes a field on their record, with the message it came from attached
- Ask for a new field in plain language, approve it, and the agent starts filling it in
- One timeline per person: every conversation, ticket, appointment, and note
- Describe an audience in plain words to get the list, then message it
- A daily list of who is waiting on a follow-up, written by the agent that spoke to them
Calendar
Real availability from your team's calendars, so an agent books meetings that hold up.
- Meetings booked inside the conversation
- Free and busy pulled from your real calendar
- Double booking is impossible by construction
- Reschedules and cancellations handled in the same thread
The handoff nobody has to build.
The usual way to get an AI agent and a working helpdesk is to buy them separately and wire them together. Then you own the wiring: two sets of permissions, two audit trails, and a sync that breaks quietly.
Escalation arrives as a ticket
With the whole conversation attached and the customer already identified. Nothing is retyped.
A qualified lead arrives as a record
In your pipeline, at the right stage, assigned to the right rep, with the transcript behind it.
One permission model
What a person can see in Operations is what they can see everywhere. There is no second access model to maintain.
One audit trail
Agent actions and human actions land in the same log, so the record of what happened is a single story.
There is no integration between the two layers, because there is nothing to integrate.
Put your AI workforce to work.
Tell us the job. We will have an agent ready to meet your customers in days, governed the way you want from day one.