Your First AI Agent: How to Build Systems of Action with Security, Governance, and Real Business Value

Building an agent has become surprisingly easy. Giving that agent access to a company’s data, customers, money, and systems — and trusting it to make decisions and execute actions — remains very difficult.

Over the past two years, the discussion around Artificial Intelligence has rapidly evolved from copilots to agents.

First, we used AI to answer questions.

Then, to summarize documents, generate code, and recommend decisions.

Now, we want it to execute.

To query systems.

To make decisions.

To open tickets.

To negotiate priorities.

To update an ERP.

To interact with customers.

To coordinate other agents.

And this is exactly the point at which the problem stops being purely technological.

Implementing the first enterprise agents is, in reality, a discussion about processes, enterprise architecture, data, security, governance, organizational change, and trust.

The first mistake many companies make is starting with the wrong question:

“Where can we use agents?”

A better question is:

Which business process or outcome justifies delegating part of its execution to an agent?

That small change completely alters the approach.

From Systems of Record to Systems of Action

For decades, companies have built large Systems of Record.

ERP.

CRM.

HCM.

SCM.

Data platforms.

These systems record the state of the organization.

They answer questions such as:

Who is our customer?

How much inventory do we have?

Which order is delayed?

Which contract is currently valid?

How much has been billed?

Who approved a given transaction?

Then came analytics, machine learning, and GenAI, expanding our ability to interpret this data.

We moved from:

Record

to:

Record → Understand

With agents, however, we enter a new phase.

Record → Understand → Decide → Act

This is where Systems of Action emerge.

A System of Action does not simply inform us that a problem exists.

It can transform context into a decision and a decision into operational action.

Consider a simple example.

A traditional system identifies:

“A strategic customer’s order is delayed.”

An analytics system may explain:

“The delay occurred because a critical supplier failed to deliver a specific component.”

An agent, however, can go further:

query alternative suppliers;

check inventory in other locations;

evaluate financial impact;

calculate alternatives;

recommend a reallocation;

request approval;

modify the order in the system;

update the ERP;

communicate with the customer;

monitor the outcome.

This distinction is fundamental.

A chatbot responds.

A System of Action changes the state of the company.

And that is exactly why security, governance, and architecture become so important.

The First Agent Is Not an AI Project

There is a natural temptation to treat agents as a new technology that needs to be deployed.

But the first enterprise agent should be treated differently:

As the controlled redesign of a business process in which part of the execution moves from people and deterministic software to a system capable of interpreting, deciding, and acting.

This means implementation should not begin with the model.

Nor the framework.

Nor the vendor.

Nor the prompt.

It should begin with the process.

Process first. Agent second. Technology third.

1. Choose the First Use Case Carefully

Not every process needs an agent.

This may be the most important principle.

A simple rule solves many problems better than an LLM.

An API is more predictable.

Traditional automation is cheaper.

A deterministic workflow is easier to test.

So before building anything, it is worth asking:

Does this problem really require autonomy?

There is a possible progression:

Automation → Assistant → Copilot → Agent → Autonomous Agent

If the need is simply to retrieve information, an assistant may be enough.

If the goal is to recommend a decision, a copilot may solve the problem.

An agent starts to make sense when the problem requires:

interpretation of context;

decisions that are not fully deterministic;

use of multiple tools;

execution of multiple steps;

adaptation based on the outcome of previous steps.

To select the first use cases, I would use an Agent Use Case Scorecard.

Evaluate:

Business ValueIs there relevant economic or operational impact?

FrequencyDoes the process occur often enough?

PainIs there rework, waiting, handoffs, or manual effort?

Task ClarityCan the objective be clearly explained?

Data ReadinessDo the necessary data exist, and are they reliable?

Tool AccessDo the required systems provide APIs or usable interfaces?

MeasurabilityCan the quality of the outcome be objectively assessed?

ReversibilityCan an error be reversed?

RiskWhat would be the impact of a wrong decision?

Human EscalationIs there someone who can take over when necessary?

For the first agent, look for:

High value + high frequency + clear process + measurable outcome + controllable risk.

Initially avoid processes with:

high ambiguity + high impact + irreversible action + poor data.

2. Model the Business Before Modeling the Agent

One of the most important lessons from mature enterprise platforms is that agents should not work directly with technical structures.

An enterprise agent should not think in terms of:

table_customer_01

or:

API_ERP_POST_837

It should understand business concepts.

Customer.

Order.

Supplier.

Invoice.

Contract.

Shipment.

And, most importantly, the relationships between them.

Customer places Order.

Supplier supplies Product.

Shipment fulfills Order.

Contract governs Customer relationship.

This semantic layer is extremely important.

It is one of the reasons Palantir’s approach around its Ontology is so interesting.

The logic can be summarized as:

Data + Logic + Actions + Security

The agent does not receive only data.

It receives a representation of the business.

And that changes everything.

3. Do Not Give Access to Systems. Give Business Capabilities.

There is a dangerous mistake in agent implementation:

giving overly generic access to tools.

For example:

Do not give the agent unrestricted access to the database.

Give it:

UpdateCustomerAddress()

Do not give it full access to the ERP.

Give it:

CreatePurchaseOrder()

Do not give it generic access to financial systems.

Give it:

RequestPaymentApproval()

Do not allow arbitrary operations on the CRM.

Give it:

CreateOpportunity()

The difference may seem small, but it is enormous.

In the first case, the agent receives an almost unlimited surface area.

In the second, it receives an explicitly defined capability.

This creates what we can call:

Bounded Agency

Autonomy within boundaries.

The agent can decide.

It can act.

But only through predefined, governed, and auditable capabilities.

4. Preserve Determinism Where Determinism Works

There is always a risk of hype around new technologies.

Agents are no exception.

The goal should not be to make every process “agentic.”

The goal should be to use autonomy only where it creates value.

A good future enterprise process will likely combine:

Deterministic Software + Agents + Humans

For example:

Invoice received↓Deterministic validation↓Exception detected↓Agent investigates↓Agent recommends resolution↓Human approves when required↓Deterministic ERP transaction

The agent enters precisely where uncertainty, context, or judgment exists.

Everything else remains deterministic.

This hybrid architecture will likely be much more common than fully autonomous processes.

5. Autonomy Must Be Earned

One of the biggest mistakes in early projects is trying to build full autonomy too soon.

I would recommend a progressive evolution.

Agent Autonomy Ladder

L0 — InformThe agent researches and summarizes.

L1 — RecommendThe agent analyzes and recommends.

L2 — PrepareThe agent prepares an action for a person.

L3 — Act with ApprovalThe agent executes after human authorization.

L4 — Bounded AutonomyThe agent acts independently within clear limits.

L5 — Autonomous WorkflowThe agent plans, coordinates, and executes, with supervision by exception.

The principle is simple:

Autonomy is earned, not granted.

Start small.

Observe.

Evaluate.

Build trust.

Increase autonomy progressively.

This model reduces risk and also improves adoption.

6. Security Changes When AI Can Act

A wrong answer from a chatbot is a problem.

A wrong action from an agent can be much more serious.

An agent may:

transfer money;

delete information;

modify an order;

cancel an operation;

send an email;

expose data;

change infrastructure;

create legal or financial commitments.

That is why, when AI moves from information to action, the security model needs to change.

Several questions become mandatory.

Agent Identity

Every agent needs its own identity.

We need to know who or what performed a specific action.

Least Privilege

The agent should have only the access required to perform its role.

Tool Authorization

Having access to a system does not mean being allowed to execute every action within it.

Secrets Management

Credentials should not be exposed in prompts or contexts.

Prompt Injection

External content should be treated as potentially untrusted.

Memory Poisoning

Incorrect or malicious memories may influence future decisions.

Human Approval

Critical actions should be able to require approval.

Circuit Breakers

Loops or anomalous behavior need limits.

Auditability

Every action needs to leave a trace.

Ideally, we should be able to answer:

Who initiated it?

What did the agent understand?

Why did it decide?

Which tools were used?

What changed?

Who approved it?

What was the outcome?

7. Think Before You Commit

For the first agents, I would strongly recommend an intermediate pattern:

Read → Reason → Simulate → Recommend → Approve → Act

Instead of:

Read → Act

Imagine a financial agent.

It identifies a potential duplicate payment.

Instead of canceling it directly, it presents:

Proposed action

Cancel payment of R$2.4M

Reason

Potential duplicate invoice.

Confidence

92%

Evidence

Invoice APurchase Order XPayment Y

Expected impact

Avoid duplicate payment.

And then:

Approve / Reject

This mechanism creates something extremely valuable:

operational trust.

8. Evals Become a Core Discipline

Traditional software is usually relatively deterministic.

Input.

Logic.

Output.

Agents do not work that way.

They may:

follow different paths;

use different tools;

perform more or fewer steps;

produce different results for similar situations.

Therefore, testing only the output is not enough.

We need to evaluate:

outcome;

reasoning trajectory;

tools used;

cost;

latency;

security;

need for human intervention.

An important practice is to create a set of real situations.

A Golden Evaluation Set.

It can begin with 50 or 100 cases.

For each case:

Input

Expected Outcome

Acceptable Behaviors

Forbidden Behaviors

Then every change in:

model,

prompt,

tool,

workflow,

architecture,

runs through the same test suite again.

A simple rule:

If you cannot measure whether the agent is getting better or worse, it is probably not ready for production.

9. Adoption Is a Human Problem

The technology may work perfectly and the project may still fail.

Why?

Because people need to trust the system.

When an agent enters a process, natural questions emerge.

Will it replace my job?

Can I trust its decisions?

Who is accountable for an error?

When should I take over?

When should I correct it?

Will my performance be compared to the agent’s?

These questions are not merely change-management details.

They are part of the operating architecture.

Adoption requires at least four elements.

Explain

Why does this agent exist?

Train

How do people work with it?

Trust

Where does it work well and where does it still fail?

Redesign

Who does what in the new process?

The future will probably not be:

Humans or AI

But:

Humans + Agents + Systems

10. Every Agent Needs an Owner

Another common mistake is treating agents as technical components.

Every enterprise agent should have a clearly defined owner.

Not only the developer.

Someone who is accountable for:

outcome;

behavior;

access;

cost;

risk;

quality;

incidents;

evolution;

eventual decommissioning.

As agents proliferate, companies will need an Agent Registry.

Something like:

Agent | Owner | Purpose | Data | Tools | Autonomy | Risk | Cost | Status

This will become increasingly important.

Because the future problem will not be simply creating agents.

It will be avoiding agent sprawl.

11. Do Not Scale Agents. Scale Standards.

Once the first agent works, a temptation emerges:

Agent 2.

Agent 3.

Agent 4.

Agent 20.

Each one developed differently.

Each one with its own security model.

Its own integration.

Its own logs.

That path does not scale.

The company needs to build a Golden Path.

Identity↓Authentication↓Data Access↓Tool Gateway↓Guardrails↓Tracing↓Evals↓Human Escalation↓Monitoring↓Cost Management

The objective is simple:

make agent number 20 easier and safer to build than agent number 1.

That is how a pilot becomes an enterprise capability.

A Playbook for the First Agents

I would organize the journey into eight stages.

1. DISCOVER

What problem or outcome do we want to change?

2. QUALIFY

Does this problem really require an agent?

3. MODEL

Which objects, relationships, decisions, and actions represent this process?

4. REDESIGN

How will humans, agents, and systems work together?

5. CONSTRAIN

Which data, tools, permissions, and autonomy levels will be allowed?

6. BUILD & EVALUATE

Build an MVP and test it against real cases.

7. PILOT

Start with supervision and progressively increase autonomy.

8. SCALE

Industrialize identity, registry, evals, observability, security, and FinOps.

In summary:

DISCOVER → QUALIFY → MODEL → REDESIGN → CONSTRAIN → BUILD → PILOT → SCALE

There is also a second progression happening in parallel:

Value → Trust → Autonomy

The more evidence the agent produces that it can reliably create value, the greater its level of autonomy can become.

The Real Enterprise Agent Stack

There is a tendency to concentrate the discussion on models.

But the model is only a small part.

An enterprise agent needs something much broader:

Context.

Semantics.

Logic.

Tools.

Actions.

Identity.

Security.

Evals.

Observability.

Governance.

At the center sits the agent.

But around it sits an entire enterprise architecture.

That is why the success of the first agents depends less on choosing “the best LLM” and much more on building this foundation correctly.

The Closed Loop

Systems of Record store what happened.

Systems of Intelligence help us understand what happened and predict what may happen.

Systems of Action change what happens next.

But there is still a fourth dimension.

Learning.

Observe↓Understand↓Decide↓Act↓Measure↓Learn↓Observe again

This closed loop may be the most important part of the transformation.

Because the organization does not merely begin automating tasks.

It starts building a system capable of continuously transforming data into decisions, decisions into actions, and actions into learning.

The Next Generation of the Enterprise

The greatest opportunity of Agentic AI will probably not be to build thousands of smarter chatbots.

It will be to create a new operational layer inside companies.

A layer capable of connecting:

Data.

Context.

Decision.

Action.

Learning.

But there is one condition.

Autonomy must come with governance.

Speed must come with control.

Intelligence must come with context.

And action must come with accountability.

The first agent matters not because it will be the most sophisticated agent in the organization.

It probably will not be.

It matters because it establishes the standards that will determine how the next fifty or five hundred agents will be built.

That is why perhaps the most important question is not:

“Which agent should we build first?”

But:

“What organizational capability do we need to build so that agents can act safely, reliably, and with real impact?”

That is the moment when a company stops simply experimenting with Agentic AI.

And actually begins building Systems of Action.