Agentic AI for your Business

Many businesses already use individual AI applications today: a chatbot answers customer questions, a generative AI model creates draft content, or an automation tool transfers data between systems. Things become more challenging when a process consists of many interdependent steps.

An example from e-commerce: A customer reports an issue with an order. The request needs to be classified, the order located in the e-commerce system, the delivery status checked, an appropriate response prepared and, if necessary, a service action initiated. The required information may be spread across several systems, which can often reach their limits in such scenarios. The key difference between automation, Gen AI and Agentic AI:

• Traditional automation works particularly well with clearly defined rules.

• Generative AI can create content and make information easier to understand.

• An AI agent goes a step further: within predefined boundaries, it can determine which steps are required to achieve a goal and use different tools and systems to do so.

This makes Agentic AI particularly relevant where complex processes, multiple handoffs and different information sources come together.

Agentic AI refers to AI systems designed to pursue a goal and independently plan and execute multiple steps within defined parameters. They can analyse information, use tools, access data and check results.

An AI agent therefore does more than simply respond to a single input. For example, it can identify which information is missing to process a request, retrieve that information from connected systems and then prepare the next appropriate step.

However, this does not mean that Agentic AI should be equated with uncontrolled end- to-end automation. In a business context, permissions, governance, monitoring and human control points must all be taken into account.

An AI agent is a system that pursues a defined goal by combining context, decision- making logic and appropriate tools.

An AI agent can be described in simple terms using five building blocks:

1. Goal: What needs to be achieved?

2. Context: What information is available?

3. Decision-making logic: What is the next appropriate step?

4. Tools: Which systems, data or APIs can the AI agent access?

5. Action: What action is it allowed to take – and when does a human need to step in?

Multiple AI agents can also work together. For example, one AI agent can analyse information while another prepares a subsequent process step.

These terms are often used interchangeably. A clear distinction can therefore help when choosing the right approach:

The boundaries between these categories can be fluid in practice. What matters is therefore the question: What task should the system solve, what decisions is it allowed to make and which systems can it access?

A typical process can be simplified as follows:

Gather information → understand the goal → plan the approach → use tools and systems → check the result → take action or hand over to a human

Imagine a business wants to use an AI agent for customer service. The AI agent receives a customer request and first determines what the issue is. It can then – provided the architecture and permissions allow it – retrieve relevant information from connected systems.

Based on this information, it can create a proposed solution or prepare a defined next step. In sensitive cases or situations that cannot be resolved with sufficient certainty, a human employee can take over.

The key difference compared with a pure chatbot is therefore the goal-oriented execution of multiple steps.

A production-ready AI agent is not made up of a single AI model. Depending on the use case, it can include the following components:

• AI models for language understanding and reasoning

• Business data and knowledge sources

• Tools and APIs for system access

• Defined permissions

• Orchestration of individual process steps

• Logging and monitoring

• Control mechanisms and human approvals
  

An AI agent platform can serve as the technical foundation for bringing these components together and managing them. For enterprise applications, the entire system landscape is therefore relevant – not just the choice of a powerful model.

A retailer with a large product portfolio wants to use AI agents:

The company has already tested its first generative AI applications:

• The marketing team uses AI to create content drafts.

• Customer service uses a chatbot to answer simple questions.

Management now wants to take the next step: AI agents should support more complex processes.

The initial wish list includes three ideas:

• an AI agent to prepare marketing campaigns,

• an AI agent for customer service and customer experience,

• an AI agent to support internal processes.

The problem: No one can reliably determine which use case should be implemented first.

Data is spread across different systems. Some APIs are available, while other interfaces would need to be assessed first. At the same time, it is unclear which actions an AI agent should be allowed to perform autonomously.

This is exactly where an Agentic AI Audit could be useful in this fictional scenario: The focus is not on the question “What can AI theoretically do?”, but rather “What is actually feasible and valuable for this business?”

Marketing involves many recurring steps: analysing briefs, bringing together product and campaign data, preparing content, creating variants and coordinating approvals.

For example, an AI agent could analyse a campaign brief, take relevant product information into account and generate prepared content variants. It could then initiate the defined approval process.

The value does not lie in automating every marketing decision. Instead, an AI agent can connect multiple preparatory steps and involve employees wherever professional or strategic decisions are required.

For our retailer, the following questions would therefore need to be clarified first:

• What data is available to the AI agent?

• Which content systems and tools can be integrated?

• Which content may be prepared automatically?

• Where is human approval required?

• How can success be measured?

E-commerce also involves processes where information from multiple sources needs to be brought together.

For example, an AI agent could analyse product information, availability and customer signals and use them to prepare a recommendation or service action.

One possible process could be:

Identify customer signal → check relevant product information → consider availability → prepare recommended action → trigger approval or defined action

Whether such a process can be automated effectively depends on the specific data, interfaces, permissions and business rules.

For AI agents for businesses, it is therefore crucial to connect the business case with the technical and organisational reality.

Customer service is another natural area of application.

An AI agent could classify a request, bring together information from multiple systems and prepare the next process step. Simple cases could follow a standardised process, while complex or sensitive cases would be handed over to a human.

This is particularly relevant because positive customer experiences often fail at system boundaries: the request may sit in the service system, order data in the commerce system and delivery information somewhere else.

An AI agent can orchestrate these process steps, provided the necessary integrations and permissions are in place.

Agentic AI does not have to start with customer-facing processes.

An example from an internal environment: A business regularly receives complex requests from different departments. Employees need to gather information from documents, knowledge bases and business systems to process them.

An AI agent could analyse the request, identify relevant information, prepare a status summary and structure the case for the responsible person. A human control point remains useful here as well: the AI agent prepares the case, while the responsible person reviews the information and decides how to proceed.

The specific business value depends on the process in question. Four typical areas of value can be distinguished:

1. Orchestrating complex processes

Challenge: A process requires information and actions from multiple systems.
AI agent: It coordinates defined process steps and uses approved tools to do so.
Result: Fewer manual handoffs within the process in question.


2. Personalising customer experiences

Challenge: Customer requests require context from different sources.
AI agent: It brings together relevant information and prepares an appropriate response or action.
Result: A more consistent and context-aware service process.


3. Supporting employees

Challenge: Professionals spend time searching, summarising and preparing information.
AI agent: It takes on defined preparatory steps.
Result: Employees can focus more on tasks that require professional judgement or decision-making.


4. Making AI initiatives more scalable

Challenge: Individual PoCs work but cannot easily be transferred to the wider enterprise environment.
AI agent: It is embedded in a defined architecture, data landscape and governance framework.
Result: A more robust foundation for further AI initiatives.

The more an AI agent interacts with enterprise systems, the more important clear control mechanisms become.

These include, for example:

• Governance: Who defines the rules and responsibilities?

• Data protection: Which data is the AI agent allowed to process?

• Permissions: Which systems and actions can it access?

• Compliance: Which regulatory requirements apply?

• Human-in-the-loop: When does a person need to review or approve an action?

• Logging: Which actions and decisions are documented?

• Testing: How is the behaviour tested before going into production?

• Monitoring: How is the solution monitored during operation?

Agentic AI should therefore not be understood as uncontrolled end-to-end automation. Especially in an enterprise environment, it is important to deliberately define boundaries, permissions and handover points.

A good starting point does not necessarily begin with the question of which model to use. It is more useful to start by looking at a specific process or use case.

The following aspects are particularly helpful:

1. A clearly defined goal: What business problem should be solved?

2. Available data: What information is needed and is it accessible?

3. Existing systems: Which applications and interfaces need to be considered?

4. Defined success criteria: How can you determine whether the use case is

   working?

5. Controlled pilot: How can the approach be validated within a defined scope?
   

The Agentic AI Audit starts with precisely this assessment. It evaluates Agentic AI readiness across five dimensions: business, processes, architecture, data and operating model. Each dimension is analysed systematically and assessed using a maturity model.

The result includes an Agentic AI Readiness Spider Map, which makes strengths, weaknesses and areas for action visible. Relevant use cases are also prioritised, while technical dependencies, risks and open questions are made transparent.

For businesses, the real challenge is often not generating ideas, but connecting the business case, technology and implementation.

A sensible approach can be structured into four phases:

1. Use case and strategy

   First, the relevant business challenges are identified and potential AI agent use cases are assessed.

2. Concept and architecture

   The next step focuses on data, systems, APIs, technical dependencies and governance.

3. Development and integration

   Based on the prioritised requirements, the technical implementation can then be planned and carried out.

4. Piloting, scaling and operations

   A successful pilot does not automatically become a scalable enterprise solution. KPIs, governance, integration and future operations should therefore also be considered.

Agentic AI can create significant value for your business when AI is expected not only to generate content but also to support and orchestrate more complex workflows.

However, the key is not to deploy an AI agent as quickly as possible. The priority is to identify the right use case and realistically assess its requirements.

The Agentic AI Audit from diva-e provides a structured foundation for doing so. It assesses business, processes, architecture, data and operating model, makes existing AI readiness visible, prioritises relevant use cases and identifies concrete areas for action and next steps.

Would you like to find out whether your business is ready for Agentic AI?