Service 05
Copilot agents and AI workflows on SharePoint
TechRam builds Copilot agents and Power Automate workflows on SharePoint that has already been cleaned up, which is what makes their answers trustworthy. This is the last step rather than the first — the agent answers from a system with owners and states, not from a folder nobody has structured.
- 4–10 weeks
Who it's for
Organisations that have the foundation in place and want AI put to work on named tasks, not demonstrated on a slide.
What we do
- Permissions and grounding checked before anything is deployed
- Agents scoped to named tasks with a measurable outcome
- Workflows where the AI step sits inside a process that already works
- Guardrails, logging and a human in the loop where it matters
How it works
Step 1
Confirm the foundation: permissions, structure, current content
Step 2
Pick the task worth automating and name the measure
Step 3
Build, test against real cases, and log what it does
Step 4
Review monthly and expand only where it earned it
The problem it answers
Most AI projects fail on the input, not the model. An agent asked a question about your operation can only answer from what it can reach, and what it can reach is whatever the permission model allows and whatever the content happens to say.
That’s why this service sits last. Copilot deployed over unknown permissions and four versions of a policy doesn’t produce a wrong answer occasionally. It produces a confident answer every time, and you have no way to tell which ones to trust.
What changes
Permissions and grounding are checked before anything is deployed. Agents are scoped to named tasks rather than switched on across the tenant, with a guardrail on what each one can reach and a person accountable for the output.
Proof: Achieve Australia
Achieve Australia ran thirty onboarding and offboarding events a month by hand, across five systems with no single record of what happened. TechRam built one pipeline that starts from a single request and provisions accounts, licences, groups and devices — recovering 120 hours a month and $126K a year.
$126K
recovered a year
120 hrs
saved every month across the onboarding lifecycle
What this does for your AI
This is the step everything else was for. The agent answers from a system with owners and states, not from a folder.
Common questions
01. How is building an agent different from our MSP turning Copilot on?
Turning Copilot on is a licence change any MSP can make in an afternoon. Making it answer correctly is this, and most of the work isn't AI work.
02. Do we have to do the other services first?
Some of them, and we'll tell you which. Permissions known, content current, one authoritative version of each policy. Without those an agent answers confidently and wrongly, which is worse than not answering.
03. What is a SharePoint agent, and when would we use one?
A SharePoint agent is a Copilot assistant grounded on the sites, libraries or files you choose, so it answers from that content rather than the whole tenant. It earns its place where people keep asking the same questions of one body of content, like policies, procedures or a service catalogue, and only once that content has one current version and an owner. Otherwise it answers confidently from the wrong document.
04. What does an AI workflow or agent cost?
Fixed scope, quoted in writing before work starts, and the number doesn't move because the work turned out harder than we expected. We don't publish a range, because the scope is what sets it and a range without a scope is a guess you would have to unlearn.
05. Do we need to buy anything new to run an agent?
Almost never. We start with what is already in your Microsoft 365 licensing, and we don't resell licences or earn a margin on them. Where something genuinely isn't covered we'll say so before you commit, not after.
06. What happens when the agent is built?
You own it: the systems, the documentation, and the ability to maintain them without us. Run & Improve exists if you would rather we kept it current, and it's monthly with no lock-in.