Understanding Future Automation for Business
The words “AI agent” were very popular in 2024. From what I see, it is a big trend for 2025. For Enterprise Architects, our job is to check new technologies and put them into our IT systems. This helps our companies change to digital ways. This guide explains AI agents in a simple way. It talks about how they work and what we, as architects, need to think about.
This is based on my own work experience.
What is an AI Agent? New Digital Helper
An AI agent is an autonomous computer program. It perceives its environment, processes information, makes decisions, and then acts autonomously to achieve predefined goals. Unlike AI chatbots, which just reply to what users say, AI agents can make their own decisions.
This helps them automate complex tasks in companies, like customer service, data analysis, or even helping with coding. They can either fully automate tasks or augment human capabilities in daily work. For instance, I’ve observed AI agents in online retail.
They recommend products, answer questions, and process orders. If they need more information, they ask the user themselves.
AI Agent vs. AI Chatbot: What is Different?
Many people use “AI agent” and “AI chatbot” as the same thing. They are alike in some ways. Both use Natural Language Processing (NLP) to understand language. Often, Large Language Models (LLMs) power them. And both can connect to other systems.
But AI agents are different in important ways.
Here is how you can tell them apart:
| Feature | AI Chatbots | AI Agents |
| Purpose | Talks to users for small tasks. | Automates big tasks, makes choices, and does many steps. |
| Decision-Making | Reacts to user input, follows rules. | Thinks, plans, and acts by itself, no direct user input. |
| Form | Usually a chat box on a website or app. | Can work in the background or be a chat interface. |
This difference is key for choosing the right solution. A sales chatbot answers questions. But a sales AI agent can guess which customers will buy more and send them personalized messages. It’s about acting first, not just reacting.
How AI Agents Work: They See, Think, Do, and Learn
To really use AI agents, we must understand how they operate. When I see them work, they always follow a clear process. It’s like a loop: they see, they process, they decide, and then they act. From a technical view, it is very smart:
- Getting Information (Perceiving): Agents first get information. This can be from sensors (like cameras in self-driving cars), from users (like voice commands), or from company data (databases, logs, documents). The Perception Module takes this raw information and makes it ready for the agent to understand.
- Thinking & Deciding (Processing & Decision-Making): After getting data, the agent analyzes it to decide what to do. They use NLP for text and check data signals. Decisions can be based on simple rules, or on Machine Learning (ML) predictions from past data. Some use Reinforcement Learning (RL), learning by trying and getting feedback. Advanced agents use Retrieval Augmented Generation (RAG). This helps them find correct information from big databases before answering, which makes answers right and reduces errors. Also, Prompt Engineering guides the LLM to think step-by-step and pick the right tools.
- Taking Action (Performing Tasks): When the agent decides, its Action Module does the task. This can be giving a text answer, controlling a device, running business processes (like orders in ERP), using APIs to connect to other systems, or managing many tasks at once. Agents can complete whole workflows.
Learning & Getting Better (Improvement): What makes AI agents smart is that they learn from what they do. They watch what happens after their actions and get better over time. This happens with feedback. Like a system that recommends things learning what you like, AI agents keep improving.
The AI Agent’s Inside Parts: How It’s Built (Technical View)
For Enterprise Architects like us, knowing how AI agents are built inside is important. It’s how the smart software works with the computer systems. There are four main parts, often made with special frameworks:
- Profiling Module: This part defines who the agent is and what its main goal is. This is set using a “system prompt” for the LLM. It also handles the first information from the environment.
- Memory Module: This is key for the agent to remember and use past information. It has two types:
- Short-Term Memory: What the agent is thinking about right now, like a recent conversation.
- Long-Term Memory: Old knowledge stored outside, like in vector databases, used with RAG to find information quickly.
- Planning Module: This is the agent’s “brain” for making strategies. It takes big goals and breaks them into smaller steps. It also chooses the right tools. LLMs can do this planning, or older AI methods can be used.
- Action Module: This part makes the agent’s decisions happen. It translates choices into commands for tools, APIs, or other systems. For robots, these are physical parts. For software, there are links to other programs.
So, Agent = Architecture + Agent Program. The architecture is the system it runs on, and the program tells it what to do.
- Key Frameworks: To build these agents, we use frameworks. These help us build faster and better:
- LangChain: A popular framework for LLM agents, memory, and tools.
- CrewAI: Good for making many AI agents work together.
- Microsoft Semantic Kernel: Helps add AI to existing apps.
- AutoGen (Microsoft): For multi-agent conversations.
Different Kinds of AI Agents
To pick the right AI agent for a business problem, we need to know different kinds exist. An agent sees, decides, and acts by itself for specific goals. Here are common types for us:
- How They Act:
- Reactive Agents: Act fast based on what they see now. Like a smart light turning on when it’s dark.
- Proactive Agents: Plan ahead for future goals. Like an agent planning a sales strategy for next month.
- Where They Work:
- Fixed Environments: Rules don’t change.
- Dynamic Environments: Always changing, agents must adapt fast.
- How Many Agents:
- Single-Agent Systems: One agent works alone.
- Multi-Agent Systems (MAS): Many agents work together to reach a big goal. They can be same or different, work together or against each other.
- Rational Agents: They always try to make the best choice.
- Other Types (based on how they are built): Simple Reflex, Model-Based Reflex, Goal-Based, Utility-Based, Learning, Belief-Desire-Intention (BDI), Logic-Based, Hierarchical Agents. Each for different kinds of tasks.
What AI Agents Can Do: Real Uses in Companies
The best thing about AI agents, for me, is how flexible they are. I’ve seen them make many different processes much smoother across all kinds of businesses. For Enterprise Architects, knowing these uses helps us find good places to start:
- Software & IT Work: Agents can be like co-pilots for coders, helping with debugging and writing code. They can also automate IT support, check system logs, and send alerts. This really helps engineers.
- Customer Service & Sales: More than just chatbots, agents improve how we talk to customers. They give personal product suggestions, find good sales leads, and talk to customers first.
- Knowledge Management: This is very helpful. Agents can find information fast from company documents, help centers, and solve problems, saving lots of time.
- Workflow Automation: This is where AI agents show their true power. They manage many steps across different systems. Like buying things for the company, helping new employees, managing deliveries, or changing prices online. It’s smart, connected automation.
- Virtual Assistants: They are like super helpers. They can plan schedules, do research, analyze data, write emails, or plan trips by themselves.
- Special Industries: They are making a big impact in areas like finance (trading, finding fraud), healthcare (patient help), and transport (traffic).
Why AI Agents Are Good for Your Company: The Benefits
Bringing AI agents into your company offers many advantages. They are flexible and can make decisions independently, speeding up tasks. They’re also versatile, so an agent built for one job can often handle others without being rebuilt.
AI agents are available 24/7, which is great for global businesses, and can save money by automating tasks that people used to do. They can complete entire workflows, from connecting with other systems to getting approvals, streamlining your processes.
They connect easily with your existing tools, making your IT systems work better together. Plus, they learn quickly, so you’ll see faster results and a quicker return on your investment.
Finally, they’re more accurate and compliant, strictly following company rules and regulations, which is key for safety and governance.
However, there are risks to consider. Compliance is a big one; agents need to follow laws like GDPR, and mistakes can lead to legal and financial issues.
Sometimes AI can make up facts (hallucinations), but this can be managed by giving agents correct information and having people check their work. It can also be hard to understand why an agent made a certain decision, especially in regulated industries.
To fix this, you should record their thought process and get human approval for important choices. Lastly, AI agents aren’t a “set and forget” solution; they need ongoing monitoring and updates as your business changes.
The Way Forward: AI Agents and the Future of ERP
The shift from simple data retrieval (RAG) to full AI Agents is a game-changer for businesses. Picture your old ERP system, once just a record keeper, becoming a super-smart, active hub. It won’t just list tasks; it’ll complete, improve, and adapt them using clever AI.
Imagine your AI-powered ERP system:
- Proactive Supply Chain: It could spot problems before they happen, adjust inventory automatically, and even negotiate with suppliers for better deals, all while following your company’s rules.
- Automated Finances: It could intelligently check bills, detect fraud, fix payment issues, and predict money trends in real-time, keeping your finances healthy and accurate.
- Enhanced Customer & Employee Experiences: AI agents could directly handle complex customer queries, take care of routine HR tasks, and offer personalized help. This frees up your team to focus on more important, creative work.
This is the future: your ERP as the central brain of your company, making operations faster, stronger, and much smarter. It empowers your people to innovate and tackle complex challenges.
So, as Enterprise Architects, how will you integrate AI Agents into your company? How do you envision them transforming your ERP systems and overall business operations? Let’s discuss and share our insights.
Learn More: Deeper into AI Agents
For Enterprise Architects and technical people who want to learn more about AI agents & SAP Business AI, here are some helpful places I have found:
- How They Work & How They’re Built:
- Tools & Libraries for Building Agents:
- LangChain Documentation: A very popular tool for making LLM agents. [ Search “LangChain agents” on their official docs (Exact URL may vary by version). ]
- Prompt Engineering Guide: LLM Agents – Learn about making agents with LLMs and how to give them good instructions. [ Link ]
- Botpress: Top AI Agent Frameworks – Compares different tools like Botpress, LangChain, CrewAI. [ Link ]
- ControlFlow: A Python tool to build AI workflows, good for control. [ Link ]
- Research & New Ideas:
- SAP Business AI | Product Resources & News: https://www.sap.com/canada/products/artificial-intelligence/resources.html
- Unlock the Power of SAP Business AI Through Learning and Certification: https://www.sap.com/documents/2025/05/7e1cdd1b-067f-0010-bca6-c68f7e60039b.html
- Artificial Intelligence | SAP Learning: https://learning.sap.com/artificial-intelligence
- Exploring the SAP Business AI Portfolio – SAP Learning: https://learning.sap.com/learning-journeys/exploring-the-sap-business-ai-portfolio-1
