AI agents with an objective, sources, permissions and supervision.
An agent should not be given autonomy just because it can generate an answer. It should operate inside a defined process, with authorised information, limits, logs and human accountability. When it does not know, it says so and hands over to a person.
Less time spent hunting for information and drafting answers — with authorised sources, limits, logs and human approval on the decisions that need it.
What we find most often.
- 01
The same question, every day
Much of internal and external support is the same questions in different words, answered by hand, one by one, by people who had more skilled work to do.
- 02
The information exists, but nobody finds it
The answer is in a procedure, a contract or an email from two years ago. Whoever needs it does not know it exists, and asks someone who will also have to search.
- 03
A justified fear of invented answers
A generic assistant always answers, even without grounds. In a proposal, a contractual deadline or a technical requirement, a plausible wrong answer costs more than no answer.
- 04
Tools with no scope and no control
Nobody defined what data the assistant can reach, what actions it can take, who supervises it or what happens when it fails. Without that, the risk cannot be assessed.
What the system includes.
Declared scope
What the agent does, what it does not do and for whom. Written before it exists and visible to the people who use it — an agent without a defined boundary cannot be assessed.
Sources your company controls
Answers are grounded in your documents and data, not in the model’s memory. That is what separates a verifiable answer from a plausible one.
Source citation
Every answer states the document it relied on, so the person receiving it can check rather than trust.
Limited permissions and actions
The agent reaches only what the asker’s profile allows, and only takes actions it has been explicitly given.
Human supervision
Decisions with financial, contractual, employment or compliance impact go, by design, through the approval of a named person.
Monitoring and evaluation
A log of interactions, detection of out-of-scope answers and a set of test cases that is run again whenever something changes.
What starts to happen on its own.
Searching documentation
Request triage
Drafting replies
Summarising, classifying and extracting
Supporting teams
Preparing documents
Recommendations subject to approval
Integrations
- Document repositories and internal procedures
- Email and messaging channels, including WhatsApp
- CRM and customer-service systems
- Databases and internal systems, read-only by default
- Existing knowledge tools and intranets
- Automation engines, for the actions the agent triggers
Security and control
- The agent reaches only the sources and data defined in its scope, with permissions by profile
- Critical decisions keep human approval, as a design rule
- Sensitive data identified up front, with explicit rules on what is never sent to an external model
- Interaction log for audit, with retention agreed with you
- Defined behaviour on failure: the agent stops and escalates instead of improvising
How we implement it.
Pick one narrow task
We start with a frequent, verifiable, low-risk task. An agent that tries to do everything can be neither assessed nor corrected.
Prepare the sources
Answer quality is capped by the quality of what the agent reads. Outdated or contradictory documents produce wrong answers that look right — and that is fixed before, not after.
Set guardrails and escalation
What the agent refuses to answer, when it hands over to a person and what it says when it does not know. This is what decides whether it is safe to put it in front of a customer.
Evaluate with real cases
A set of questions with known answers, run before go-live and whenever the sources or the model change. Without measurement, “looks good” is all you have.
Go live with close follow-up
It starts under close supervision with interaction reviews, and the scope widens as the behaviour is confirmed.
Where we apply this solution.
- 01pt-MZ
Mozambique
We work with companies that already have demand and need structure: bringing requests, customers, suppliers and operations into one system, without replacing everything that already works.
Explore Mozambique (in Portuguese) - 02pt-PT
Portugal
We do not propose replacing what already works. We connect your existing systems, automate the work between them and help free qualified people’s hours for qualified work.
Explore Portugal (in Portuguese) - 03pt-BR
Brazil
We design for higher-volume operations and distributed teams, where a minute of manual work is multiplied by every transaction.
Explore Brazil (in Portuguese) - 04EN
Global
Ten expansion markets, from Switzerland to Singapore, with the same discipline: diagnostic first, a price per phase once the scope is known, and human approval on critical decisions.
Explore Global
Know where to intervene before you buy technology.
The diagnostic turns a loosely defined problem into an executive decision with the process, priorities, risks, options and clear next steps.
Assess a critical processA focused conversation. A defined problem. Clear next steps.
What you receive
- 01Problem definition and current map
- 02Inventory of relevant systems and data
- 03Baseline and blocker matrix
- 04Risks and dependencies
- 05Conceptual design of the future process
- 06Options and priorities
- 07Conceptual architecture
- 08Parametric estimate and assumptions
- 09Roadmap and executive recommendation
- 10Decision on a pilot or implementation
About AI agents.
That is not the goal we work to, and it would not be honest to promise you either way. What we do is remove repetitive tasks and improve access to information. What the company does with the time freed up is a management decision for you — not a technical outcome we can guarantee in either direction.
Three ways, and none of them is trust. Answers are grounded in your sources and cite the original document, so you can check. The agent is instructed to refuse what is out of scope and to escalate rather than guess. And there is a set of test cases with known answers that is run whenever something changes. We reduce the risk and make it verifiable; we do not eliminate it, and anyone who says they do is selling you something else.
That is an architecture decision we make with you before choosing the technology, based on how sensitive the data is. There are options with a contractual no-training commitment and self-hosted options, with different costs and capabilities. We set out the alternatives and what each one implies, rather than choosing for you.
It depends on the scope and the risk. For factual information about documented statuses and procedures, often yes. For prices, contractual deadlines, complaints or any binding commitment, we recommend a draft with human review. If the risk of a wrong answer is high, we say so rather than switch it on and hope.
A specific objective and an owner; identified, authorised sources; minimum information quality; defined users and permissions; quality criteria; error handling and escalation; human approval and monitoring.
When the process is not defined, there is no reliable source, the output cannot be reviewed, the risk of error is unacceptable, a simple rule would do, the benefit does not pay for the complexity, or rights and obligations have not been assessed.
By usage, time saved, completion, quality, human corrections, failures, escalations, cost per task, satisfaction and incidents.
Before you buy more technology, find out where to intervene first.
Share the process you want to improve. AGORAMOZ will analyse the problem, its feasibility and the recommended next step.
No sales pressure · Diagnostic before proposal