Service
AI Agents
From answering questions to getting work done.
AI agents can go beyond generating responses. They can interpret objectives, work with information, use connected systems and carry out defined tasks.
AI Transformation Consulting helps organisations identify where agentic AI can create genuine value and design practical approaches for introducing it safely and effectively.
What is an AI agent?
AI agents can act, not just respond.
- 01
Generative AI
Produces text, images or code when asked.
- 02
AI assistant
Helps a person with a task, one prompt at a time.
- 03
AI agent
Works towards a goal, uses tools and carries out defined actions.
What an agent can do
Capable, but not magic.
Within clear limits, an AI agent can potentially:
- —Understand a goal
- —Gather information
- —Reason about a task
- —Use tools and systems
- —Complete defined actions
- —Check results
- —Escalate when human judgement is needed
Agents are not fully autonomous. They work best with clear boundaries and people in the loop.
Where agents can create value
Potential uses for AI agents
Customer service
Resolving routine requests end to end, with hand-off when needed.
Sales support
Researching accounts and preparing for conversations.
Research
Gathering, comparing and summarising sources.
Internal knowledge
Finding answers across scattered systems.
Service operations
Coordinating routine steps across teams.
IT support
Diagnosing and fixing common issues.
Case management
Pulling together context and next actions.
Document workflows
Moving documents through review and approval.
Employee support
Handling everyday HR and policy queries.
Data analysis
Running defined analyses and explaining results.
Commercial operations
Supporting quoting, renewals and account admin.
These are potential use cases, not client case studies.
Should this be an agent?
Not every AI problem needs an agent.
The right answer might instead be:
- —A conventional application
- —Automation
- —A chatbot
- —A generative AI feature
- —Better information management
- —Process redesign
- —A human-led workflow
The aim is to solve the business problem, not to deploy an agent because agents are fashionable.
Designing an AI agent
Six questions every agent design must answer
- 01
Objective
What is the agent trying to achieve?
- 02
Knowledge
What information can it use?
- 03
Tools
What systems can it access?
- 04
Actions
What is it allowed to do?
- 05
Controls
What requires approval?
- 06
Measurement
How will performance be assessed?
Agentic AI in the enterprise
What it takes to run agents safely
- —Identity and access
- —Data security
- —Permissions
- —Governance
- —Human oversight
- —Accuracy
- —Auditability
- —Integration
- —Reliability
- —Cost
- —Risk
From pilot to production
A good demo isn't a working solution.
An impressive prototype can hide real problems: edge cases, security, cost and how it fits with existing systems. Getting to production takes deliberate steps.
- 01
Prototype
- 02
Test
- 03
Govern
- 04
Integrate
- 05
Deploy
- 06
Monitor
- 07
Improve
Business value
Where agents can make a difference
- —Faster service
- —Greater employee capacity
- —Less manual work
- —Better access to information
- —Better responsiveness
- —Scalable operations
- —New customer experiences
We won't promise full autonomy or savings we can't stand behind.
Our approach
How we introduce AI agents
Agents usually sit inside a wider change, linked to AI transformation and AI automation.
- 01
Identify
- 02
Assess
- 03
Design
- 04
Prototype
- 05
Govern
- 06
Deploy
- 07
Improve