AI Agents
Autonomous or semi-autonomous AI workflows
AI agents can do more than answer questions. They can understand a goal, gather information, use business systems and carry out a series of tasks with varying levels of human oversight.
But an AI agent isn't automatically the right solution.
The first step is understanding what you're trying to achieve.
Tell us what you're trying to achieve. We'll help work out whether an AI agent, automation, integration or another approach makes sense.
What is an AI agent?
An AI agent is a software system that can be given a goal or task and then take a series of actions to achieve it, rather than simply generating a single response.
Depending on the system, an AI agent may be able to:
- Understand instructions
- Break a task into steps
- Gather information
- Search internal or external sources
- Use business applications
- Analyse information
- Make decisions within defined boundaries
- Take actions
- Check results
- Ask for clarification
- Escalate to a person
"AI agent" means different things in different products and implementations. Some agents are tightly scoped to one task; others coordinate several steps across business systems. None of them have unlimited autonomy — what they can do is set by how they are designed, what they can access and the controls around them.
A chatbot answers a question.
An automation follows a predefined workflow.
An AI agent can potentially decide what steps are needed to achieve a goal and carry them out.
In practice these technologies overlap. Many business AI agents combine conversation, fixed automation steps and AI-driven decisions in one system.
How do AI agents work?
Most AI agent systems follow a similar basic pattern:
GoalUnderstandPlanActCheckContinue or escalate
Goal
The agent is given an objective.
Understand
It interprets the request and the relevant information.
Plan
It works out the steps required.
Act
It uses the tools, systems or information sources it has been given access to.
Check
It evaluates the result and decides what should happen next.
Continue or escalate
If a decision needs human judgement or falls outside defined boundaries, the work is handed to a person.
The exact capabilities depend on how the agent is designed, the tools it can access and the controls surrounding it.
AI agents vs chatbots
| Chatbot | AI agent |
|---|---|
| Primarily responds to questions | Can pursue a defined goal |
| Usually conversation-focused | Can perform multiple actions |
| Often follows a defined flow | Can determine the next step |
| Provides information | Can potentially use information to take action |
| Usually limited to the conversation | Can interact with business systems |
The distinction isn't always absolute. Modern chatbots can perform actions, and some systems marketed as agents use relatively fixed workflows. The point is to understand the concept, not to draw an artificial technical line.
AI agents vs traditional automation
Traditional automation works particularly well when the process is predictable:
Form submittedCreate CRM recordSend email
An AI agent becomes more useful where the process requires interpretation, research, decisions or adapting to different situations:
Research a prospectUnderstand their businessReview previous interactionsIdentify opportunitiesPrepare a briefingRecommend next action
AI agents and traditional automation can be combined — an agent handles the judgement-heavy steps while fixed AI automation and workflow tools handle the predictable ones.
The question isn't whether you should use an AI agent. It's what is the most effective way to achieve the business outcome.
What can AI agents do for businesses?
Practical AI agents for business tend to fall into a few areas. Each could potentially:
Sales agents
- Research prospects
- Build account briefs
- Identify relevant contacts
- Analyse CRM information
- Prepare meeting briefings
- Draft personalised outreach
- Summarise sales conversations
- Identify follow-up actions
Customer service agents
- Understand customer enquiries
- Retrieve relevant information
- Resolve routine requests
- Update customer records
- Escalate complex cases
- Summarise interactions
Research agents
- Research markets
- Monitor competitors
- Gather information
- Compare sources
- Produce research summaries
- Monitor changes
Operations agents
- Monitor workflows
- Process requests
- Check information
- Coordinate activities
- Identify exceptions
- Escalate issues
Finance and administration agents
- Review documents
- Extract information
- Investigate discrepancies
- Prepare reports
- Handle routine requests
- Support financial analysis
Internal knowledge agents
- Search company information
- Answer employee questions
- Find policies and documents
- Summarise internal knowledge
- Support onboarding
What an agent can actually do depends on the systems, permissions, data and controls available to it.
Example: an AI sales agent
A business wants to reduce the time its sales team spends researching prospects. Today, a salesperson manually handles:
- Finding company information
- Researching the prospect
- Reviewing CRM history
- Looking at previous conversations
- Identifying relevant opportunities
- Preparing a briefing
An AI agent could potentially coordinate these steps:
New prospectResearch companyCheck CRMAnalyse informationIdentify opportunitiesPrepare briefingSalesperson reviews
The salesperson remains responsible for the important commercial decisions. The agent handles much of the preparation.
This is the type of business problem where an AI agent may be worth investigating.
What is an autonomous AI agent?
Autonomy describes how much of a process an AI system can carry out without a person directing every individual step. It is a spectrum, not a switch:
Human-controlledHuman-approvedSemi-autonomousMore autonomous
Human-controlled
The AI suggests what to do.
Human-approved
The AI prepares an action, but a person approves it.
Semi-autonomous
The AI carries out defined actions and escalates certain decisions.
More autonomous
The AI manages a larger workflow within predefined rules and controls.
More autonomy isn't automatically better. The right level depends on the risk, complexity and consequences of the decisions being made.
When is an AI agent the right solution?
Good candidates for AI agent solutions often involve:
Multiple steps
The task requires several connected activities.
Information gathering
The system needs to find and interpret information before acting.
Variable inputs
Every situation isn't exactly the same.
Repetitive knowledge work
People repeatedly perform similar research or decision-support tasks.
Multiple systems
The process involves several applications or information sources.
A measurable outcome
The business can define what success looks like.
Appropriate human oversight
There is a clear way to involve people where judgement or approval is required.
When an AI agent may not be the answer
An AI agent can add unnecessary complexity where a simpler solution would work. It may not be appropriate when:
- A straightforward rule-based automation would solve the problem.
- The process happens too infrequently.
- The underlying process is poorly designed.
- Data is unreliable or inaccessible.
- Systems cannot be integrated.
- The cost of implementation outweighs the potential benefit.
- Decisions require human judgement.
- The consequences of an incorrect action are too significant without appropriate controls.
Sometimes the right answer is traditional automation.
Sometimes it is better data.
Sometimes it is a process redesign.
Sometimes it is an AI application rather than an agent.
And sometimes an AI agent is exactly what is required.
AI agent risks and governance
Because agents can take actions rather than simply generate information, they need appropriate controls. Practical areas to plan for:
- Access permissions
- Data security
- Privacy
- Human oversight
- Approval points
- Audit trails
- Testing
- Monitoring
- Error handling
- Escalation
- Clear boundaries
- Accountability
The more consequential the action, the more important appropriate controls and human oversight become.
What does an AI agent consultant do?
An AI agent consultant helps determine whether an agent is appropriate and, if so, how it should fit into the business. Typical AI agent consulting work includes:
1.Understand the business objective
What outcome is the business trying to achieve?
2.Identify the process
What happens today? Where does the work start and finish?
3.Determine where decisions occur
Which decisions can be automated? Which need human involvement?
4.Identify systems and data
Which applications and information sources would the agent need to access?
5.Define the agent's role
What should it be allowed to do? What should it never do?
6.Design the workflow
What should happen at each stage?
7.Define controls
Where should people review, approve or intervene?
8.Determine implementation requirements
What technology, integration, data and specialist skills are required?
This often sits alongside a wider AI strategy or AI transformation programme.
AI agent development
Building an AI agent can involve several disciplines. Potential requirements include:
- AI and LLM expertise
- Workflow design
- Software development
- API integration
- Data engineering
- Knowledge management
- Security
- Governance
- User experience
- Testing and monitoring
You may not need all of these skills for every project. The assessment helps determine what expertise is actually relevant.
How much does an AI agent cost?
There is no sensible fixed price. Cost depends on:
- Complexity
- Number of workflows
- Number of users
- AI model usage
- Integrations
- Data requirements
- Security requirements
- Development effort
- Testing
- Monitoring
- Ongoing support
A simple internal agent can be very different from a business-critical autonomous system integrated across multiple enterprise platforms.
The sensible starting point is to understand the business case and requirements before estimating implementation cost.
How to start with AI agents
Start with the business problem, not the technology. Ask:
- What are people spending too much time doing?
- Where does work involve repeated research or information gathering?
- Where do employees repeatedly make similar decisions?
- Where does a process involve several systems or applications?
- Where could a person review the result rather than perform every step themselves?
These questions can reveal potential agent opportunities — or show that a simpler approach would work better.
Do you need an AI agent consultant?
You may benefit from specialist help if:
- You have identified a process that could potentially be handled by an AI agent.
- You aren't sure whether an agent is actually the right technology.
- You want to understand the business case before building anything.
- You need an agent to work across existing business systems.
- You are considering autonomous or semi-autonomous workflows.
- You need to understand security and governance requirements.
- You have experimented with AI agents but aren't sure how to take them into production.
- You need to bring together AI, data, software and integration expertise.
Find out whether you actually need an AI agent
You don't need to decide whether you need an AI agent, an automation specialist, an AI strategist, an AI developer or an integration specialist.
That's what the assessment is for.
Tell us what you're trying to achieve. We'll ask you a few questions, identify the potential opportunity and explain what type of AI expertise may be relevant.
- Here's what we think you need.
- Here's why.
- Here's who can help.