Digital employees for finance: What AI agents really are - and what they can truly accomplish.
Digital employees for finance: What AI agents really are - and what they can truly accomplish.
Digital employees for finance: What AI agents really are - and what they can truly accomplish.
Digital employees for finance: What AI agents really are - and what they can truly accomplish.
4 Aug 2025
Artificial Intelligence is far more than just a buzzword - and never before has its applicability for businesses been so tangible and the results so directly measurable as they are today.
However, while many decision-makers in the Finance have already gained experience with automated processes or tools like ChatGPT, a new chapter begins with agentic AI: AI agents become digital employees of financial teams - with clearly specified role profiles, their own tools, and the ability to think and act independently.
In this article, we provide you with a fundamental overview: What is an AI agent? What distinguishes it? And how does it differ from conventional AI or previous chatbots?
What is an AI agent - and what does “agentic” actually mean?
An AI agent is a form of software, equipped with a combination of artificial intelligence, process knowledge, and action competence: An agent is not limited to merely delivering information - it can also independently perform tasks, react to outcomes, and plan next steps. This makes it “agentic”.
In contrast to classic rule-based automations or generative AI models (like those we know from ChatGPT), an AI agent is capable of making decisions and initiating actions within a process - just as a competent employee would.
LLMs as the Heart: Why Large Language Models are so Important
The reasoning ability of an AI agent is based on a large LLM, a large language model (as used in ChatGPT, for example). This model analyses complex information, interprets context, makes decisions, and plans the next steps.
But unlike ChatGPT, which typically responds isolated to text inputs, the agent is integrated into your system landscape. It can, for example:
interact with ERP or banking systems via APIs
automatically retrieve or process data
make suggestions for daily disposition plans
initiate bank transactions
An Agent is Like a Digital Employee - with Responsibility, Knowledge, and Tools
At Flowzar, we think of AI agents as new team members:
Role profile: Each agent has a clearly defined area of responsibility - e.g., “Cash Manager” or “Accounts Payable Manager”.
Expertise: The agent brings a solid foundational understanding of financial processes - such as payment standards, accounting rules, or currency exchange effects.
Company Rules: Additionally, it learns company-specific guidelines - e.g., cash requirements, payment approvals, or supplier relationships.
Tools: Through integrations, it accesses ERP systems, banking interfaces, Excel documents, or even external data sources.
The result: An agent works contextually, with sound judgement and comprehensibly - just as you would expect from a new employee.
Onboarding Instead of Hiring: Faster Results
An essential advantage: Agents are not “hired”, but “onboarded”. They have a clearly defined role profile and receive appropriate system access and - where sensible - even their own email address to, for instance, ask questions of employees or suppliers.
The path to the first measurable results from agents in a company is thus considerably shorter than with human employees. No job advertisements, no lengthy training. Significantly lower initial costs and risks of hiring the wrong person.
Deterministic vs. Agentic: Why the Right Balance Matters
Not every process needs to be solved “intelligently”. Many processes in finance follow fixed rules - such as checking minimum requirements of cash in company accounts. Here, deterministic (i.e., rule-based) automations are ideal.
But as soon as context, interpretation, or decision is required - for instance, when matching outstanding items in complex intercompany structures - an AI agent can play to its strengths.
At Flowzar, we follow the philosophy: Deterministic where possible - agentic where necessary.
This ensures that financial processes are designed to be efficient, robust, and intelligent - with full control over the degree of automation.
Explainability, Autonomy & Compliance: What Decision-Makers Should Know
Especially in finance, transparency, audit trails, and compliance are essential. Therefore, our agents are designed so that every decision is systemically documented, explainable, and traceable if necessary - including the thought paths that led to a particular action.
Important: The degree of autonomy of an AI agent is configurable. Depending on the use case, the agent can:
complete tasks fully automated
present suggestions for decisions
adopt a human-in-the-loop approach in a hybrid approach - reversible actions with low risk are automated, others are presented for decision-making
Agentic AI is thus not a loss of control - but an active decision about what actions require which competences.
Conclusion: AI Agents are the Next Evolutionary Step in Finance
While traditional software operates solely on rules, which require immense manual effort for all “special cases”, and chatbots at best provide responses, AI agents take active roles in processes - tailored to the process architecture and logic of the company itself.
Especially for companies in the manufacturing industry with complex supply chains and international dependencies, this opens up entirely new possibilities: More efficient processes, better-informed decisions, and a new quality of automation.
Artificial Intelligence is far more than just a buzzword - and never before has its applicability for businesses been so tangible and the results so directly measurable as they are today.
However, while many decision-makers in the Finance have already gained experience with automated processes or tools like ChatGPT, a new chapter begins with agentic AI: AI agents become digital employees of financial teams - with clearly specified role profiles, their own tools, and the ability to think and act independently.
In this article, we provide you with a fundamental overview: What is an AI agent? What distinguishes it? And how does it differ from conventional AI or previous chatbots?
What is an AI agent - and what does “agentic” actually mean?
An AI agent is a form of software, equipped with a combination of artificial intelligence, process knowledge, and action competence: An agent is not limited to merely delivering information - it can also independently perform tasks, react to outcomes, and plan next steps. This makes it “agentic”.
In contrast to classic rule-based automations or generative AI models (like those we know from ChatGPT), an AI agent is capable of making decisions and initiating actions within a process - just as a competent employee would.
LLMs as the Heart: Why Large Language Models are so Important
The reasoning ability of an AI agent is based on a large LLM, a large language model (as used in ChatGPT, for example). This model analyses complex information, interprets context, makes decisions, and plans the next steps.
But unlike ChatGPT, which typically responds isolated to text inputs, the agent is integrated into your system landscape. It can, for example:
interact with ERP or banking systems via APIs
automatically retrieve or process data
make suggestions for daily disposition plans
initiate bank transactions
An Agent is Like a Digital Employee - with Responsibility, Knowledge, and Tools
At Flowzar, we think of AI agents as new team members:
Role profile: Each agent has a clearly defined area of responsibility - e.g., “Cash Manager” or “Accounts Payable Manager”.
Expertise: The agent brings a solid foundational understanding of financial processes - such as payment standards, accounting rules, or currency exchange effects.
Company Rules: Additionally, it learns company-specific guidelines - e.g., cash requirements, payment approvals, or supplier relationships.
Tools: Through integrations, it accesses ERP systems, banking interfaces, Excel documents, or even external data sources.
The result: An agent works contextually, with sound judgement and comprehensibly - just as you would expect from a new employee.
Onboarding Instead of Hiring: Faster Results
An essential advantage: Agents are not “hired”, but “onboarded”. They have a clearly defined role profile and receive appropriate system access and - where sensible - even their own email address to, for instance, ask questions of employees or suppliers.
The path to the first measurable results from agents in a company is thus considerably shorter than with human employees. No job advertisements, no lengthy training. Significantly lower initial costs and risks of hiring the wrong person.
Deterministic vs. Agentic: Why the Right Balance Matters
Not every process needs to be solved “intelligently”. Many processes in finance follow fixed rules - such as checking minimum requirements of cash in company accounts. Here, deterministic (i.e., rule-based) automations are ideal.
But as soon as context, interpretation, or decision is required - for instance, when matching outstanding items in complex intercompany structures - an AI agent can play to its strengths.
At Flowzar, we follow the philosophy: Deterministic where possible - agentic where necessary.
This ensures that financial processes are designed to be efficient, robust, and intelligent - with full control over the degree of automation.
Explainability, Autonomy & Compliance: What Decision-Makers Should Know
Especially in finance, transparency, audit trails, and compliance are essential. Therefore, our agents are designed so that every decision is systemically documented, explainable, and traceable if necessary - including the thought paths that led to a particular action.
Important: The degree of autonomy of an AI agent is configurable. Depending on the use case, the agent can:
complete tasks fully automated
present suggestions for decisions
adopt a human-in-the-loop approach in a hybrid approach - reversible actions with low risk are automated, others are presented for decision-making
Agentic AI is thus not a loss of control - but an active decision about what actions require which competences.
Conclusion: AI Agents are the Next Evolutionary Step in Finance
While traditional software operates solely on rules, which require immense manual effort for all “special cases”, and chatbots at best provide responses, AI agents take active roles in processes - tailored to the process architecture and logic of the company itself.
Especially for companies in the manufacturing industry with complex supply chains and international dependencies, this opens up entirely new possibilities: More efficient processes, better-informed decisions, and a new quality of automation.
Artificial Intelligence is far more than just a buzzword - and never before has its applicability for businesses been so tangible and the results so directly measurable as they are today.
However, while many decision-makers in the Finance have already gained experience with automated processes or tools like ChatGPT, a new chapter begins with agentic AI: AI agents become digital employees of financial teams - with clearly specified role profiles, their own tools, and the ability to think and act independently.
In this article, we provide you with a fundamental overview: What is an AI agent? What distinguishes it? And how does it differ from conventional AI or previous chatbots?
What is an AI agent - and what does “agentic” actually mean?
An AI agent is a form of software, equipped with a combination of artificial intelligence, process knowledge, and action competence: An agent is not limited to merely delivering information - it can also independently perform tasks, react to outcomes, and plan next steps. This makes it “agentic”.
In contrast to classic rule-based automations or generative AI models (like those we know from ChatGPT), an AI agent is capable of making decisions and initiating actions within a process - just as a competent employee would.
LLMs as the Heart: Why Large Language Models are so Important
The reasoning ability of an AI agent is based on a large LLM, a large language model (as used in ChatGPT, for example). This model analyses complex information, interprets context, makes decisions, and plans the next steps.
But unlike ChatGPT, which typically responds isolated to text inputs, the agent is integrated into your system landscape. It can, for example:
interact with ERP or banking systems via APIs
automatically retrieve or process data
make suggestions for daily disposition plans
initiate bank transactions
An Agent is Like a Digital Employee - with Responsibility, Knowledge, and Tools
At Flowzar, we think of AI agents as new team members:
Role profile: Each agent has a clearly defined area of responsibility - e.g., “Cash Manager” or “Accounts Payable Manager”.
Expertise: The agent brings a solid foundational understanding of financial processes - such as payment standards, accounting rules, or currency exchange effects.
Company Rules: Additionally, it learns company-specific guidelines - e.g., cash requirements, payment approvals, or supplier relationships.
Tools: Through integrations, it accesses ERP systems, banking interfaces, Excel documents, or even external data sources.
The result: An agent works contextually, with sound judgement and comprehensibly - just as you would expect from a new employee.
Onboarding Instead of Hiring: Faster Results
An essential advantage: Agents are not “hired”, but “onboarded”. They have a clearly defined role profile and receive appropriate system access and - where sensible - even their own email address to, for instance, ask questions of employees or suppliers.
The path to the first measurable results from agents in a company is thus considerably shorter than with human employees. No job advertisements, no lengthy training. Significantly lower initial costs and risks of hiring the wrong person.
Deterministic vs. Agentic: Why the Right Balance Matters
Not every process needs to be solved “intelligently”. Many processes in finance follow fixed rules - such as checking minimum requirements of cash in company accounts. Here, deterministic (i.e., rule-based) automations are ideal.
But as soon as context, interpretation, or decision is required - for instance, when matching outstanding items in complex intercompany structures - an AI agent can play to its strengths.
At Flowzar, we follow the philosophy: Deterministic where possible - agentic where necessary.
This ensures that financial processes are designed to be efficient, robust, and intelligent - with full control over the degree of automation.
Explainability, Autonomy & Compliance: What Decision-Makers Should Know
Especially in finance, transparency, audit trails, and compliance are essential. Therefore, our agents are designed so that every decision is systemically documented, explainable, and traceable if necessary - including the thought paths that led to a particular action.
Important: The degree of autonomy of an AI agent is configurable. Depending on the use case, the agent can:
complete tasks fully automated
present suggestions for decisions
adopt a human-in-the-loop approach in a hybrid approach - reversible actions with low risk are automated, others are presented for decision-making
Agentic AI is thus not a loss of control - but an active decision about what actions require which competences.
Conclusion: AI Agents are the Next Evolutionary Step in Finance
While traditional software operates solely on rules, which require immense manual effort for all “special cases”, and chatbots at best provide responses, AI agents take active roles in processes - tailored to the process architecture and logic of the company itself.
Especially for companies in the manufacturing industry with complex supply chains and international dependencies, this opens up entirely new possibilities: More efficient processes, better-informed decisions, and a new quality of automation.