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What is an AI agent?
An AI agent is an autonomous software system that independently plans and executes a task, utilizing various tools, data sources, and systems in the process. At its core is typically a Large Language Model (LLM) that makes decisions and orchestrates actions.
Unlike a traditional AI chatbot, an agent does more than just answer questions. It pursues a goal, decides on the next steps, calls APIs or tools, evaluates results, and adjusts its plan as needed.
Often, multiple agents work together in a multi-agent system: one plans, one searches, and one verifies. This architecture is also known as agentic AI.
For small and medium-sized businesses, AI agents thus form the foundation for the next wave of automation: processes that were previously too complex for traditional RPA can now, for the first time, be fully automated thanks to language understanding and contextual awareness.
Best Practices for Deploying AI Agents
In successful agent projects, six principles have proven effective in making the difference between a demo and actual operation:
- Start small: Focus on a clearly defined use case first—such as quote generation or email triage.
- Keep tools separate: Each tool has a clear purpose and documented limitations.
- Set guardrails: What is the agent not allowed to do? Which actions require approval? Define these in advance.
- Log everything: Without traceability, there’s no productive use—and no compliance.
- Roll out iteratively: First a recommendation, then a proposal with approval, then autonomous operation within a narrow scope.
- Build in evaluations: Automated tests measure quality and security during ongoing operations.
Common Mistakes in AI Agent Projects
Many agent prototypes get stuck in the sandbox. This is usually due to one of the following reasons:
- Too broad a scope: If you try to automate everything at once, you’ll fail at the first edge case.
- Poor tool design: If tools return unclear results, the agent gets tangled up.
- Lack of governance: Without clear rules about what the agent is allowed to do, deployment to production is blocked.
- No cost control: Agents can quickly become expensive in long chains. Budget limits are mandatory.
- No monitoring: What isn’t measured deteriorates unnoticed.
Agent vs. Chatbot vs. RPA
These three technologies are often lumped together. They differ significantly in terms of autonomy and areas of application:
- Chatbot: Answers questions and conducts dialogs. Does not perform independent actions within the system.
- RPA: Automates clearly defined, rule-based steps on screens and within systems.
- AI agent: Plans flexibly, makes situational decisions, and uses tools—even for unstructured tasks.
Deploying AI Agents Effectively
In a free initial consultation, we’ll analyze your processes and identify the use cases where an AI agent can have the greatest impact. We’ll then outline a concrete implementation plan for you.
As an AI partner for small and medium-sized businesses, we’ll take your AI agents from prototype to reliable operation—including governance, monitoring, and operations.
What We Offer
- AI Agents for Businesses — Consulting, Design, and Implementation of Productive Agents.
- AI Agent for Procurement — a concrete use case with a high ROI.
- Agentic AI Use Case — Reference project and detailed approach.
- Checklist: AI Agent Readiness — free self-assessment available for download.
