AI Agents & Knowledge Systems
AI agents built around real workflows
An AI agent is software that interprets a request and takes steps towards completing it, using information it has been given access to. In service and support it is useful when the same questions recur, the knowledge behind the answer is trustworthy, and it is clear where a human must take over.
The problem we usually meet
Agents are often specified as a technology purchase rather than a workflow decision, and the questions that determine whether one will work are left until late.
Those questions are simple: what information does it need, who will use it, what may it decide alone, what must go to a person, and what happens when it does not know.
Who this is for
- Service teams answering similar customer questions repeatedly from documented knowledge.
- Support organisations with a large body of internal knowledge that is hard to search.
- Customer service, customer support and technical support functions with high routine volumes.
- Anyone who has been sold an agent and wants to check the workflow logic first.
What we help with
- Deciding whether an agent is the right answer at all.
- Internal knowledge assistants grounded in approved documentation.
- Customer support assistants working from FAQs, knowledge base and product content.
- Research and analysis assistants that gather and summarise from trusted sources.
- Defining permissions, escalation paths and human review points.
- Evaluating answer quality before and after rollout.
Examples of practical agents
Internal knowledge assistant
One place for employees to ask about policies, processes and documentation, using approved internal information.
Customer support assistant
Helps customers directly or helps agents draft answers faster, with escalation when confidence is low.
Research assistant
Gathers, compares and summarises information from defined sources to support a decision.
How an engagement runs
- 01
Workflow first
Establish the task, the users and where judgement is required.
- 02
Information
Identify the sources the agent may use and how current they are.
- 03
Boundaries
Define permissions, escalation and what the agent must not do.
- 04
Build
Create a working version grounded in the agreed sources.
- 05
Evaluate
Test answer quality against real questions, including the difficult ones.
- 06
Adopt
Train users, document the limits and review results over time.
What you can expect to end up with
- A clear decision on whether an agent is justified for this workflow.
- If it is, a working agent grounded in defined, approved information.
- Documented boundaries, escalation paths and review responsibilities.
- An evaluation approach you can keep using as content changes.
Tools and methods we work with
- Retrieval over approved internal content, commonly called RAG
- Microsoft 365 and Copilot-based assistants
- Knowledge base structuring and content preparation
- Permission and access design
- Answer-quality evaluation sets
- Human-in-the-loop workflow design
When an agent is not the answer
If the underlying information is inconsistent, out of date or contradictory, an agent will present those problems confidently and at speed. If the task follows fixed rules, automation is cheaper and more predictable. If the volume is low, the maintenance rarely pays back. We say so before building.
Frequently asked questions
What is an AI agent?
An AI agent is software that interprets a request and carries out steps towards completing it, drawing on the information and tools it has been given access to. Unlike fixed automation, it decides how to respond rather than following one predefined path.
How is an agent different from a chatbot?
A traditional chatbot follows scripted paths. An agent works from your actual information and can handle questions that were not scripted, which makes source quality and clear boundaries far more important.
What information does an agent need?
Sources that are accurate, current and permitted for the intended audience. Preparing that content is often the largest part of the work, and it is usually valuable regardless of whether an agent is built.
How do you stop an agent from giving wrong answers?
By grounding it in approved sources, restricting scope, defining what it must escalate, testing against real questions including edge cases, and keeping a person accountable for the output where it matters.
Can an agent work with existing Microsoft 365 content?
Often yes, provided permissions and content quality are in order. That review is part of the work and frequently improves everyday findability too.
Related
Where is the work getting stuck?
Bring us a workflow, process or AI opportunity and we will look at what is happening before deciding what belongs there.
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