Agentic Business Processes: How they work & why they matter

What are Agentic Business Processes?

Agentic Business Processes are business processes in which specialised (AI) agents work together to gather information, interpret context, make decisions and perform specific tasks to deliver specific business outcomes.

Instead of relying on one general-purpose AI model, multiple specialised agents work together to gather information, interpret context, perform tasks and produce reliable results.

The process is designed around a business goal, with the right combination of AI, software, algorithms and human oversight.

Why do Agentic Business Processes Matter?

Agentic Business Processes create business value by turn complex work into structured, repeatable processes. They help organisations reduce manual effort, scale expertise and deliver measurable business value.

Reduce manual work and costs – Automate repetitive tasks such as gathering, checking and processing information.

Make better-informed decisions – Give teams faster access to relevant information, context and insights.

Scale organisational expertise – Make specialised knowledge available across teams and apply it consistently.

Read our perspective on why the hourly-fee business is changing and why organisations need to find new ways to scale their know-how.

How do Agentic Business Processes work?

Instead of asking one general-purpose AI model to handle an entire workflow, an Agentic Business Process divides the work into specialised tasks. Different (AI) agents are responsible for different parts of the process and work together to produce a reliable business outcome.

Think of it as a team of specialists rather than one generalist.

One agent might retrieve information. Another determines whether that information is relevant. Another identifies relationships between pieces of information. Another may generate an explanation or recommendation.

The result is not simply an AI-generated answer. It is a business process designed around a specific outcome.

A simplified process might look like this:

Data Ingestion → Entity Recognition → Relevance Analysis → Context & Relationships → Business Insight → Action

Each stage has a specific purpose.

FOR EXAMPLE

Data ingestion collects information from relevant internal and external sources.
An Entity Agent identifies companies, products, people, topics or other relevant entities.
A Relevance Agent filters information using statistical or deterministic methods.
A Clustering or Context Agent identifies relationships and connects information to existing knowledge.
A final stage transforms the verified information into a business insight, recommendation or action.

This architecture means that every part of the process can use the technology that is best suited to the task.

An LLM may be useful for understanding language. A statistical model may be better at detecting patterns. A database may be better at storing relationships. Conventional software may be the best solution for calculations, sorting or validation.

Agentic does not mean using AI everywhere. It means using the right intelligence at the right point in the process.

AI Agents vs. Agentic Business Processes

The terms AI agent and Agentic Business Process are closely related, but they are not the same thing.

An AI agent is an intelligent software component capable of performing a task, often by interpreting information and deciding what to do next.

An Agentic Business Process goes a step further.

It connects multiple capabilities into a structured business process with a defined purpose, context, data sources, rules and outcomes.

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AGENTIC BUSINESS PROCESSES vs AGENTIC AI

AI Agent	Agentic Business Process
Focuses on a task	Focuses on a business outcome
May operate independently	Coordinates multiple specialised capabilities
Often works with a limited context	Uses business-specific context and data
Produces an answer or action	Produces a repeatable business result
Can be generic	Designed around a specific organisation or department
Agent-level governance	Process-level governance and oversight

This distinction matters.

A collection of AI agents does not automatically create an Agentic Business Process. The agents need to work together within an architecture that makes sense for the actual business problem.

Hybrid intelligence: using AI where it adds value

An effective Agentic Business Process can combine:

specialised language models
deterministic algorithms
statistical analysis
traditional software
structured databases
graph databases
vector search
human expertise and validation

For example, a small language model may identify an entity in a document, while deterministic rules validate the result. A graph database can then provide additional context by connecting that entity to related companies, products or events.

This combination creates what we call hybrid intelligence.

The objective is not to maximise the amount of AI in a process.

The objective is to maximise the quality and usefulness of the business outcome.

What makes an Agentic Business Process trustworthy?

Giving AI more autonomy also makes governance more important.

A production-ready Agentic Business Process should therefore not only be intelligent. It should be traceable, controlled and explainable.

Important considerations include:

What data did an agent use?
Where did the information originate?
Which agent performed which task?
Which rules or models were applied?
How was the result validated?
When should a human intervene?
What happens when the system is uncertain?

This is why an Agentic Business Process should be designed as an architecture rather than simply assembled from AI agents.

The process needs clear responsibilities, defined boundaries and appropriate human oversight.

Agentic Business Processes in practice: DPG Media

A practical example of an Agentic Business Process is the work Kentivo developed with DPG Media.

CHALLENGE

The challenge was simple: sales teams need to stay up to date with constantly changing market developments, but finding, analysing and translating relevant information into useful sales talking points takes time.

SOLUTION

Kentivo built an Agentic Business Process that continuously collects, filters and analyses market information and transforms relevant developments into actionable sales talking points.

BUSINESS VALUE

Less manual research and faster access to relevant sales intelligence.

The result: From 6 to 17 industries covered. Reports that previously took weeks can now be produced in minutes.

Read the full DPG Media case story →

How Kentivo builds Agentic Business Processes

Kentivo uses its proprietary Genie AI platform to build and orchestrate specialised agents, data sources, models and algorithms around specific business goals.

Each component has a defined role and uses the technology best suited to the task. This modular approach allows processes to evolve without rebuilding the entire solution.

Built for production use, with traceability and European data governance, Genie enables organisations to start with a specific business challenge and expand when it makes sense.

From concept to implementation

Want to know how Agentic Business Processes are put into practice? Read more about our Agentic Business Process Implementation approach, from the initial scoping workshop to building and deploying a working solution.

Tailored to your team. Built around your data. Affordable to implement.

That is how Kentivo turns Agentic AI into practical business processes.

Ready to apply Agentic Business Processes?

Let’s discuss your business challenge!

Kentivo helps you scope, design, build and implement solutions around your organisation’s needs.

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DPG use case

Technology in service of clarity