Read and summarize
Long documents, threads, and attachments become a short, checkable summary, so people decide faster.
AI agents
AI that works inside the process, not beside it
A Mogno agent is not a chat window. It is a governed participant in the workflow, with an objective, scoped data, permissions, and a record. It reads, compares, and follows up, and it pauses for a person when the decision carries risk.
Every agent runs inside a workflow your IT can see, scope, and audit.
Why it is different
You have seen AI demos. This one runs in production.
Loose tools and copilots help one person with one task. They never carry the responsibility of a real process. This is the gap between a prompt and an agent that operates.
A loose prompt
A Mogno agent
- A prompt typed into a chat.
- An answer with no trail.
- Data copied and pasted by hand.
- No approval policy.
- No operational ownership.
- Definition An agent with an objective, inputs, outputs, permissions, and a record.
- Record A structured execution with author, time, context, and result.
- Data Data reached through grants and DataPacks, never scraped.
- Approval Sensitive actions wait for a named approver.
- Ownership The flow has an owner, an SLA, a fallback, and a value report.
What agents do
The invisible work agents take off your team
Agents earn their place by removing the manual work that lives between systems: reading, consolidating, chasing, and drafting. People keep the judgment; the agent clears the repetition.
Turn documents into data
Fields are pulled from files into the DataPack, with validation on the ones that matter.
Compare and flag
Proposals, versions, and records are cross-checked, and inconsistencies surface before they turn into rework.
Follow up and draft
Reminders go out within policy, and first drafts of replies or opinions are prepared for a person to review.
Governed autonomy
You decide how far the agent goes
Autonomy is not all or nothing. Each action gets the independence it can safely carry. Low-risk work runs on its own; anything sensitive waits for a person. The line is set by policy, not by the model.
Agent acts
Summarize a document
AutomaticCuts reading time and helps the user decide sooner.
Extract fields
Automatic, validatedTurns a document into operational data, with checks on the critical fields.
Flag an inconsistency
Automatic, no final callFinds risk early, before it becomes rework. A person still decides.
Send a reminder
Automatic, within policyClears manual follow-up without anyone chasing it.
Prepare an opinion
Draft for reviewSpeeds up the analysis without outsourcing the judgment.
Approve a purchase, payment, or contract
Always humanGovernance keeps responsibility and audit with a named person.
Write back to a system of record
Human or explicit policyA sensitive action needs deliberate, scoped control.
Human decides
How it stays safe
Every agent runs under the same controls
An agent is only as safe as the platform around it. On Mogno, agents share one governance model with apps, workflows, and data, so none of them run outside it.
See the full security storyOn by default
FAQ
What teams ask about agents
How is this different from ChatGPT or Copilot?
Those help one person with one task. A Mogno agent operates inside a process, with data, permissions, approvals, and a record, so its work is auditable and part of the operation, not a side conversation.
AI makes mistakes. How do you keep that in check?
That is exactly why agents do not decide sensitive actions on their own. They summarize, classify, compare, and prepare; a person approves wherever there is risk, and every step is recorded.
Can an agent act fully autonomously?
Only where policy allows it, and only for low-risk actions. Anything that carries money, legal, or data risk pauses for a named approver. You set the line per action, per workspace.
What data can an agent see?
Only what its grant allows, through DataPacks scoped to the workflow. Agents do not roam your systems; they reach defined data inside one tenant.
Do our credentials go into the model?
No. Credentials are resolved server-side at execution time and never enter the agent reasoning context or the app code.
Can we use our own AI provider and models?
Yes. Agents run on the platform control plane, and the model provider stays your choice, without locking your workflows to it.
Get started
Put an agent to work where the manual work is.
Pick one process buried in reading, chasing, and copying, and watch an agent operate inside it, governed from the first run.