What are
Agentic Systems ?

Imagine a group of smart interns, each specialized in a different task, working together to solve complex problems. That's what an agentic system is in the world of AI. Instead of one big AI trying to do everything, we use multiple smaller AIs, called agents, each with its own role. They collaborate, just like a human team would, to tackle challenging tasks more effectively.

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Why Agentic Systems? Overcoming AI Limitations

Traditional AI, like chatbots, can sometimes give incorrect information or get confused. Agentic systems solve this by having different agents check each other's work. It's like having one intern write a report and another fact-check it. This teamwork approach makes the results more reliable and reduces mistakes significantly.

Customized for Your Needs: Tailored AI Solutions

Every business has unique challenges. Agentic systems can be customized to fit your specific needs. We break down complex tasks into smaller parts and assign specialized agents to each. This allows us to create AI solutions that work precisely for your business, whether it's analyzing legal documents, processing data, or automating customer service.

Measurable and Optimizable: AI That We Can Fine-Tune

One of the best things about agentic systems is that we can measure how well they're performing. We set clear goals and constantly test the system's output. This means you can trust the results and see the value it brings to your business. While these systems don't learn on their own, our experts can fine-tune them based on performance data, updating instructions, and incorporating new capabilities as AI technology advances.

Limitations and Precautions of Generative AI

Despite its numerous promises and revolutionary potential, generative artificial intelligence also has significant limitations that are crucial to keep in mind during its evaluation and deployment:

Bias and Inaccuracies

Bias and Inaccuracies

A generative model can reflect biases present in its training data, leading to erroneous or misleading results.

Lack of True Understanding

Lack of True Understanding

Generative AI does not truly "understand" the deep meaning of what it generates; it merely predicts statistical patterns.

Need for Human Oversight

Need for Human Oversight

The outputs of a generative model should always be checked and validated by a human expert before use.

At Bot Resources, we are aware of these challenges and leverage our expertise to ensure a responsible and controlled deployment of generative AI in business settings.

Ready to learn more about Generative AI?

You want to deepen your understanding of generative AI? Contact us to explore how generative models work, see real-world applications, and discuss how AI can transform your business.
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