Enterprise
Multi-Agent Enterprise Assistant on Microsoft 365
Narrow, auditable AI agents that act on the data your organization already has in Microsoft 365, and only ever see what each user is allowed to see.
Who it is for
Typically an IT or operations leader at a mid-size enterprise already on Microsoft 365, who has tried or evaluated Copilot and found it too generic for their team's repetitive workflows, and who needs any AI tooling to pass a real security and compliance review.
First working version
12 to 16 weeks
A planning estimate, firmed up once we see your real stack.
Foundation we would extend
Alfred OS
Our own Alfred OS, a multi-agent platform built on FastAPI and Temporal workflows, is in active development. Its architecture is the starting point we'd extend, not a finished product to resell. See our products
The problem
Who has this problem, and why it matters
Organizations already live inside Microsoft 365: email, Teams, SharePoint and documents. They want AI agents that actually act on that data, finding a document, summarizing a thread, drafting a report or routing a request.
But they cannot send sensitive company information to an uncontrolled tool, and a single general chatbot is not enough for real workflows. Every serious deployment has to get past a security and compliance review first.
Market
Where existing tools stop
Microsoft's own Copilot is the obvious incumbent. It is well integrated but general-purpose, and not tailored to any one organization's own workflows or internal processes.
The opening is the specific, narrow agents a particular organization actually needs, built on the same permission and data infrastructure. A one-size-fits-all product cannot do that for every customer.
This is our read of the market, not a formal study.
The solution
What we would build, in order
- 01
Secure data layer
Access to Microsoft 365 data through Microsoft Graph, always respecting each user's existing permissions.
- 02
First agent
One agent for one high-value workflow, such as document search and answer, or recurring reporting.
- 03
Multiple agents
Specialized agents coordinated by an orchestrator, each with limited, auditable permissions.
- 04
Admin and control
Admin controls, usage analytics, cost tracking and approval steps for sensitive actions.
Architecture
How it works, end to end
User asks in Teams or a web app
The request carries the user's identity from Microsoft Entra ID (Azure AD).
Orchestrator selects an agent
A coordinating step routes the request to the narrow agent built for that task.
Permission-aware retrieval
The agent reads data through Microsoft Graph as the user, so it can only see what that user can open.
Model inside the tenant
Azure OpenAI Service processes the request inside the organization's Azure environment.
Approval gate
Sensitive actions, such as sending an email or changing a record, wait for a human to confirm.
Audit log
Every request, source document and action is recorded for the security team.
Tech stack
What we would use, and why
Data access
Microsoft Graph API
One interface to mail, files, Teams and SharePoint, with permissions intact.
Model
Azure OpenAI Service
Keeps data inside the organization's Azure tenant, which is what security teams ask for first.
Identity
Microsoft Entra ID (Azure AD)
Acts on behalf of the signed-in user, so access follows existing permissions.
Backend
Python (FastAPI)
A good fit for agent orchestration and the AI ecosystem.
Retrieval
Vector store with access-control metadata
Search results are filtered by who is asking, not just by relevance.
Workflows
A durable workflow engine such as Temporal
Long-running agent tasks survive restarts and can pause for approval.
Prototype vs production
A demo is not a product
You can prototype this yourself
- +A single agent answering questions over a handful of exported documents
- +No real permissions involved, so it can only be tried on non-sensitive material
- +Useful to show the team what an assistant could do
We encourage it. A working prototype is the best way to find out what you actually want.
What production needs
Permission-aware retrieval
A user must never get an answer drawn from a file they cannot open. A single leak is the failure that ends the engagement.
Tenant and identity integration
Acting as the signed-in user, handling consent and admin approval in the customer's own tenant.
Data residency
Many organizations require data to stay in specific regions and never leave their environment.
Prompt-injection defenses
A document or email can contain instructions aimed at the agent. Content has to be treated as data, never as commands.
Action limits and approvals
Each agent gets the minimum permissions for its job, and sensitive actions need explicit confirmation.
Full audit logging
Security teams need to answer who asked what, what was read and what was done.
Evaluation at scale
Quality must be measured on real tasks before and after every change.
Rate limits and cost caps
A busy day or a looping agent should not become a surprise bill.
Our approach
Where we would start
We would build the permission-aware retrieval layer before any agent logic. If an answer can come from a document the user cannot open, nothing built on top matters, so this is proven first with the organization's own permission model.
We would also bring a security review checklist to the first scoping call. In enterprise AI projects the security and compliance review is usually what stalls things long after the build is otherwise ready, so we would treat it as part of the project plan from the start.
FAQ
Common questions
How is this different from Microsoft Copilot?+
Copilot is broad and general-purpose. This is about narrow agents built for your specific workflows, on the same permission and data foundations. Many organizations use both.
Can the agent see data the user is not allowed to see?+
It should never be able to. The design acts on behalf of the signed-in user through Microsoft Graph, and retrieval is filtered by that user's permissions. Proving this is the first thing we would build and test.
Does our data leave our Azure environment?+
The intended design keeps processing inside your Azure tenant using Azure OpenAI Service. We would confirm residency and retention requirements with your security team during scoping.
How long does a first version take?+
Our planning estimate is 12 to 16 weeks for a first production agent, with the architecture ready for more. The security review runs alongside, and its timing depends on your organization.
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Bring your prototype
A half-formed idea, a working demo or a full spec. We will tell you honestly what is worth building first and what you can safely keep doing yourself. Typical response under 2 hours.