Process automation / AI agents
AI agents with
judgment.
An AI agent isn’t a chatbot with more small talk. It perceives information, reasons about it, uses your tools and executes actions —always within clear limits and with a person supervising the steps that matter.
What it is
Perceive, reason,
act and respond.
An AI agent is a system that resolves a task end to end with a degree of autonomy: itperceives (reads free text, emails, data), reasons about what it has read,uses tools (queries your systems and calls APIs) and executes actions —all within defined limits and with human approval on the critical steps.
The difference from a chatbot is clear: the chatbot converses with scripted answers and stops there. The difference from an RPA is clear too: RPA repeats clicks and fixed tasks, but it doesn’t understand an email written in natural language or decide what to do with a new case. An agent does: it interprets, chooses the right action and knows when to stop and hand off to a person. It is part of our process automation practice, and it fits where fixed rules fall short: wherever you have to read, decide and stay accountable.
Problems it solves
When fixed rules
fall short.
Tasks that require reading free text
Emails, tickets and forms written in human language. Fixed rules are not enough: you have to understand what the person means, not just what they type.
Volume peaks
Campaigns, month-end closes, high season. Work arrives in waves and the team can’t keep up, right when responding well matters most.
The 24/7 response expectation
Customers write at any hour and don’t want to wait until the next day. Covering every time slot with people is expensive and inefficient.
Repetitive back-office that needs judgment
Tasks that follow a pattern but require interpreting each case: copying data isn’t enough, you have to decide what to do with it.
First-line queries that flood the team
The same questions over and over steal time from work that truly requires experience. The team fights fires instead of moving forward.
Scattered knowledge
The answer exists —in a PDF, a manual, a history log— but finding it costs more than giving it. The know-how is trapped in documents and people.
What they can do
From incoming message
to resolved action.
A well-designed agent chains perception, decision and action without friction. In practice, this is what it does:
- First-line (L1) customer service
- Triage and classification of tickets and emails
- Answers grounded in your knowledge base
- Data extraction and entry into your systems
- Orchestration of actions via API
- Drafting of replies and summaries
- Proactive follow-ups and reminders
- Escalation to a person when appropriate
Use cases by department
An agent for every
area.
| Department | What the agent does | Result |
|---|---|---|
| Customer service | Resolves common queries, checks an order status and opens tickets | Instant answers, any time; only complex cases reach a person. |
| Sales | Qualifies inbound leads, answers product questions and books meetings | No lead goes cold; reps receive opportunities already filtered and prioritised. |
| Internal support / IT | Handles team requests (access, passwords, system questions) and resolves or routes | A decongested service desk and less internal waiting time. |
| Finance | Reads invoices, matches data against the order and prepares the record for approval | Less typing, fewer errors and a faster close. |
| HR | Guides onboarding, answers policy questions and collects documentation | Consistent onboarding and fewer repeated questions to the people team. |
The core idea
A good agent doesn’t improvise: it decides with judgment and stays accountable.
Benefits and results
What you can
expect.
Available 24/7
The agent never sleeps or takes a break. It handles a query at three in the morning and a Monday peak with the same quality.
Immediate response
No queues, no waiting times. The first answer arrives in seconds, when the person’s interest is at its highest.
Consistency
The same question always gets the best available answer, regardless of who is on shift that day.
Scales without linear cost
Going from 100 to 10,000 conversations doesn’t require a hundred times more hires. Capacity grows without multiplying headcount.
Your team, on the exceptions
The agent absorbs the repetitive work. People spend their judgment on the cases that truly need it.
Improves with use
Every conversation reveals gaps in the knowledge base and new cases to refine. The agent becomes more useful over time.
How we build them safely
Autonomy, yes.
No free rein.
A useful agent is an agent you can trust. These are the principles we apply to everything we build:
Guardrails and action limits
The agent can only do what we explicitly authorise. No actions outside its scope, no dangerous improvisation.
Human in the loop
On critical steps —high amounts, customer offboarding, sensitive data— the final decision belongs to a person. The agent prepares, the human approves.
Answers grounded in sources
The agent answers from your knowledge base, not from its imagination, and can point to where each fact came from.
Evaluations (evals)
We test the agent against real cases before and after every change. We measure its reliability with numbers, not impressions.
Least privilege
It accesses only the data and systems strictly needed for its task, not one more. Less exposure surface.
Traceability and GDPR
Every decision and action is logged and auditable. We process data in accordance with GDPR and sign the required agreements.
Common integrations
It connects with what
you already use.
Using a tool that isn’t on the list? If it has an API or webhooks, we can almost always connect it.
The CPPA method
From use case to an agent
that stays accountable.
Mapping use cases and data
We identify which queries and tasks the agent will handle, which knowledge sources it will rely on and which systems it needs to touch. We measure current volume and effort to set a baseline.
Agent and guardrail design
We define its scope, its tone, what it can and cannot do and —crucially— where it must stop and ask for human approval. Safety is designed in from the start, not bolted on at the end.
Build, evaluate and integrate
We connect the agent to your tools through robust APIs and put it through evaluations with real cases. It doesn’t go live until it performs reliably and measurably.
Operate, measure and improve
We launch in phases, with observability from day one. We measure resolution, escalations and satisfaction, and continuously refine the knowledge base and guardrails.
Agent examples
How it looks
in practice.
First-line support agent
- A query arrives
- Understands the intent
- Searches the knowledge base
- Answers with the solution
- Resolves or opens a ticket
- Escalates to a person if needed
Invoice triage agent
- Receives the invoice
- Extracts the key data
- Matches against the order
- Flags discrepancies
- Prepares the record
- Sends for human approval
HR onboarding agent
- New hire
- Generates the checklist
- Requests the documentation
- Answers policy questions
- Creates access via API
- Notifies the responsible person
Lead qualification agent
- A lead comes in
- Enriches the data
- Asks qualifying questions
- Scores against criteria
- Books a meeting or discards
- Assigns to the rep
Risks and mitigation
Let’s talk about what
can go wrong.
An agent without controls is a risk. That’s why every known risk has its mitigation designed in advance:
Hallucinations (making up data)
Mitigated with answers grounded in verified sources and continuous evaluations that detect when the agent drifts or answers without a basis.
Wrong actions
Strict action limits and human approval on every irreversible or high-impact step. The agent proposes; the person confirms what matters.
Data leakage
Least privilege, encryption in transit and at rest, and processing in accordance with GDPR. The agent only sees what it needs for its task.
Dependency and “black box”
Full observability, auditable logs and a human fallback always available. If the agent fails, there is a clear plan that doesn’t break the service.
Off-tone or off-topic answers
Style and content guardrails, plus a testing phase with real cases before the agent ever talks to a customer.
Automating the mess
Before connecting the agent, we tidy the process and the knowledge base it will use. An agent built on chaotic data inherits the chaos.
Frequently asked questions
Will an agent replace my team?
What is the difference between an agent, a chatbot and RPA?
Does it hallucinate? Is it reliable?
Is it secure and GDPR-compliant?
What does it integrate with?
How long until it is in production?
Related services
Keep exploring.
Workflow automation
When a process chains tasks and systems end to end, an automated workflow orchestrates it —and an agent handles the steps that need judgment.
See →
Business process automation
If the challenge isn’t a task but a full end-to-end process, BPA redesigns it entirely, with agents where they add value.
See →
Document automation
Many agents start by reading a document (invoice, contract, form). Document automation extracts and classifies that data reliably.
See →Which task could an agent take on?
Request a proposal →Want to see how we think about a connected end-to-end system? Read ourCPPA X-RAY on 100 Montaditos.