AI agents
Systems capable of interpreting information, using tools and executing tasks within defined limits.
- # Interpretation
- # System queries
- # Rule-based execution
- # Escalation to people
The problem
Some processes are not covered by classic automation: the information arrives as free text, each case is a little different and solving it means checking two or three places before deciding what to do.
These are exactly the processes that end up in the hands of an experienced person, doing the same thing with small variations, and the ones that get stuck the most when that person isn't around.
The solution
An agent interprets the information that comes in, queries your systems, applies the business rules we define with you and executes the action that applies. All within explicit limits: what it can touch, what it can't and how far it can go on its own.
We don't build one just because. An agent only comes in where it adds something a deterministic automation can't give, and always with a record of what it does and a path to escalate to a person.
What is an enterprise AI agent?
An agent isn't a chatbot that answers questions. It's a component that works inside your operation and that can:
Interpret information
Read an email, a document or a form and understand what is being asked.
Query your systems
Look up in the ERP, the CRM or a database the data it needs in order to decide.
Use tools
Work with the same applications and services your team uses, with its own permissions.
Execute actions
Create a record, prepare a document, send a notice or trigger the next step of the process.
Check the result
Verify that what it has done came out right before considering it closed.
Follow rules
Respect the business limits we set: amounts, deadlines, suppliers, types of operation.
Escalate to a person
When a case falls outside what was expected, stop and hand it to someone on your team with the context ready.
Where they fit
- Emails
Classify incoming email, extract what is relevant and prepare or route the handling.
- Documents
Analyze contracts, invoices or files and pull out the data the process needs.
- Quotes
Prepare proposals from the request and the ERP data, ready for review.
- Incidents
Log, classify, check the history and propose the next step.
- Research
Gather and cross-check information from several sources and deliver it organized.
- CRM
Complete and update records, log interactions and prioritize the follow-up.
- Administrative processes
Chain several steps, systems and checks that a person does by hand today.
- Requests
Handle the intake, resolve what is within its limits and route the rest with context.
These are examples of use cases, not off-the-shelf catalog products. Each agent is designed around the company's real process, with permissions, limits, validations, traceability and escalation to a person defined from the start.
When is an automation enough and when do you need an agent?
The difference isn't the technology, it's the process. If the steps are always the same, an agent is unnecessary: it adds cost and variability without adding anything.
The process always does the same thing
The steps are defined in advance and there is nothing to interpret. It is cheaper, faster and more predictable. When it works, it is the right option.
The process has to be understood before it can be executed
Information arrives unstructured, each case varies and several sources have to be checked before deciding. That's where an agent gives what a fixed rule can't.
Define the process
We define which cases come in, which decisions are taken and where the agent's responsibility ends.
Rules and permissions
We set which systems it can query, which actions it can execute and which limits it can't cross.
Testing with real cases
We validate it with your company's historical cases before it touches anything in production.
Monitoring and tuning
We review the exceptions and refine the rules as new situations come up.
- Can an AI agent make mistakes?
- Yes, like any system. That's why we don't leave it loose: it works with explicit business rules, with scoped permissions, checking the result of what it does and with the instruction to stop and escalate when a case falls outside what was expected.
- How is it different from a chatbot?
- A chatbot talks. An agent executes: it queries your systems, applies rules and carries out actions within the process. The conversation, if there is one, is only the surface.
- Do I need an agent or is automation enough?
- If the process can be written as a sequence of fixed steps, automation is enough and works out better. An agent makes sense when information has to be interpreted or decisions chained depending on the case.
- Can what it does be supervised?
- Yes, and it is part of the design. There is a record of what it queried, what it decided and what it executed. On critical steps it also leaves the operation ready for a person to approve.
- What happens with my company's data?
- The agent accesses only what it needs for its task, with its own revocable credentials. We choose tools and settings that don't use your information to train models, and we design the solution with data protection and the requirements of each project in mind.