Enterprise AI Agent: which architecture to choose in 2026?

What is an AI agent in a business context?

An AI agent is a system capable of using context, interpreting a situation, and executing certain actions to achieve a goal.

The difference with traditional automation lies mainly in the amount of decision-making power left to the system.

An automation generally follows deterministic logic:

If X happens → do Y.

For example:

Deal won → create customer project → notify the delivery team.

The expected outcome is known in advance. Adding an AI agent here would mostly introduce variability.

An agent becomes more interesting when the process requires interpretation:

Analyze a deal's history → identify the main risks → propose the next action → prepare a brief for the sales representative.

In between the two, an AI assistant can analyze, summarize, or suggest without executing the important actions itself.

Before deploying an AI agent: do you really need one?

This is often the first decision to make.

Let’s look at a sales use case.

If the rule is:

Company with more than 50 employees + country France → assign to the Enterprise team

automation is sufficient.

If qualification requires analyzing the company's website, its activity, the description of the need, the CRM history, and multiple signals before proposing a priority, an agent starts to bring value.

So the question is not:

Where can we add AI?

But:

Which part of the process actually requires understanding context or choosing between multiple actions?

This is also the logic we apply when working on automations and AI agents integrated into business processes: start from the process before choosing the technology.

Where should an AI agent live in the company?

Once the use case is identified, several architectures are possible.

1. Directly in the CRM or business tool

This is often the first approach to consider when the necessary data and users already work within the same system.

Airtable is a good example. Its Field Agents allow users to analyze, search, or generate information directly at the operational data level. Omni can also interact with the base and assist in building or using the application.

We detail these possibilities and their limitations in our guide on Airtable AI.

Attio follows a similar logic on the CRM side: its Workflows can combine sales context, agents, search, third-party applications, and actions on data.

An agent can, for example, analyze an account, qualify a lead, or prepare a sales action from the information already present in the CRM.

The main benefit is simple: the context, the users, and the actions are already in the same place.

Creating an additional agent platform is not necessarily useful if the need remains entirely within this environment.

2. A cross-functional agent platform

The situation changes when the agent needs to work with multiple systems.

Imagine a team that wants to be able to ask:

What is the situation with the ACME account and what do we need to act on this week?

The answer may require:

• opportunities from the CRM;

• the latest internal exchanges;

• documents from Google Drive;

• support tickets;

• meeting notes;

• information from multiple teams.

The problem is no longer just adding AI to a CRM.

The agent must be allowed to work with a cross-functional company context.

Platforms like Dust address this logic by connecting different sources and enabling teams to create and share agents with common access rules.

This architecture becomes relevant when multiple silos genuinely need to be crossed.

However, it adds a new platform to maintain. If the agent ultimately works almost exclusively in a single CRM or business tool, this layer may be unnecessary.

3. Ready-to-use business agents

Another approach is to use agents that are already specialized in certain roles.

Solutions like Limova offer agents tailored for prospecting, marketing, recruiting, or other relatively standardized functions.

The company does not necessarily build its own agentic architecture. Instead, it aims to quickly automate a well-defined need.

This approach can be interesting when the actual process corresponds closely to the workflow provided by the solution.

It becomes less suitable when the business rules, data, or workflows of the company are highly specific.

The key criterion is therefore not the number of available features, but the gap between the process offered by the tool and your actual process.

4. A custom agent

Some situations require more control.

A custom-built agent can be relevant when it needs to:

• use multiple proprietary systems;

• apply highly specific business rules;

• be integrated directly into a product;

• perform critical actions;

• comply with a specific architecture or technical constraints.

The system can then combine APIs, AI models, MCP, context storage, orchestrators, logs, and validations.

But this freedom comes at a cost: each component will need to be developed, monitored, and maintained.

Custom-built is therefore not automatically a more mature architecture.

If the native capabilities of a tool properly cover the need, they are often preferable.

MCP can also serve as an intermediate layer to allow an external agent to use existing tools. We explain this logic in more detail in our guide on Airtable MCP.

How to choose the right architecture?

Rather than starting by comparing platforms, it is more useful to answer a few questions.

The same company can, of course, use multiple approaches.

For example, an agent in Attio can analyze the context of an opportunity and recommend the next action.

When the deal then moves to Won, a deterministic automation can create the corresponding project in the delivery tool.

AI only gets involved where interpretation is useful.

4 examples of actually useful AI agents

Qualifying a lead from multiple signals

A web form rarely contains all the information needed for a relevant qualification.

An agent can analyze the expressed need, search for the company's activity, leverage information already present in the CRM, and suggest a priority level.

The CRM then stores the result, and an automation can take over for routing.

The value of the agent here comes from its ability to interpret several imperfect signals, rather than simply applying a scoring formula.

Preparing the follow-up of an opportunity

An agent integrated into the CRM can analyze emails, meetings, notes, and recent changes to the deal.

It can then generate:

• a summary of the context;

• identified risks;

• missing information;

• a suggested next action.

The sales representative retains the final decision on important actions.

The agent mainly reduces the time needed to rebuild context before a follow-up or a meeting.

Monitoring a customer account

After the signing, an agent can help detect signals that are difficult to track manually.

For example, it can analyze the account's recent activity, interactions, contextual changes, and certain operational indicators to identify:

• a risk of churn;

• an expansion opportunity;

• an account with no interaction for too long;

• a situation that deserves the CSM's attention.

The agent does not necessarily have to decide on the next action on its own. It can simply bring up the accounts that require human analysis.

Searching for scattered information across the company

Last case: the necessary knowledge is distributed across several tools.

An employee asks a question, and the agent must search through documentation, the CRM, internal chats, and potentially support tickets before responding.

A cross-functional platform is much more coherent here than an agent locked inside a single business tool.

It is precisely in this type of situation that the choice of architecture becomes more important than the choice of the AI model itself.

What safeguards should be put in place?

An agent preparing an internal summary does not carry the same level of risk as an agent capable of sending an email, modifying financial data, or triggering an irreversible action.

Before going live, five questions must be explicitly answered:

1. What data can the agent access?

2. What actions can it execute on its own?

3. Which actions require human validation?

4. Can we understand what it did in case of an error?

5. Who maintains the agent when the process changes?

The right level of autonomy directly depends on the risk involved.

Qualification, summaries, or drafts can tolerate more variability.

Data deletion, billing, or contractual commitments generally require tighter control.

The goal is not to make the agent as autonomous as possible, but to give it just enough autonomy to improve the process without losing control over it.

Start with the process, not the platform

Airtable, Attio, Dust, Limova, or a custom-built architecture can all be relevant in certain contexts.

But none of them should be the starting point.

Instead, start by identifying:

the process → what is repetitive → what requires interpretation → the necessary data → the authorized actions → the expected level of control.

Then, look at whether your CRM or business tool can already cover the need properly.

Add a cross-functional platform or a specific architecture only when a real limitation arises.

The challenge with AI agents in business is not to build the most advanced system. It is to build the simplest system that actually improves the teams' work.

Frequently asked questions about AI agents in business