FAQs

Basic agentic workflows are becoming more accessible, but building complex, secure, and reliable agents for enterprise use requires technical expertise. It involves setting up the right tools, APIs, and guardrails. This is why partnering with experts is often the most efficient route to adoption.

You must be vigilant about the data you feed the AI. If your historical data is biased, the AI's output will be too. It is essential to audit the AI's suggestions and specifically ask the model to consider diverse perspectives or "Red Team" its own output for potential bias.

RAG is a method of connecting an AI model to your private, trusted data (like company PDFs, databases, or emails). It is crucial for business problem solving because it prevents the AI from guessing or "hallucinating" facts. It ensures the solutions provided are relevant to your specific business context.

No, and it shouldn't. AI excels at processing data, identifying patterns, and generating options. However, it lacks moral judgment, deep contextual understanding of human relationships, and accountability. AI should be viewed as a tool to inform and challenge human intuition, not replace it.

The Tree of Thoughts technique forces the AI to explore multiple lines of reasoning rather than just predicting the next word. By breaking a problem into branches and exploring sub-solutions for each branch, the AI can evaluate the validity of different paths before committing to a final answer, resulting in much higher logic quality.

Generative AI typically refers to models that create content (text, images) based on a user prompt. Agentic AI takes this a step further by having the ability to break down a goal, plan a sequence of actions, and use external tools (like web search or APIs) to execute tasks autonomously to solve a problem.

If you use the free, public versions, your data may be used to train the model. For business use, you should use Enterprise versions or API-based implementations that explicitly state data is not retained for training.

You cannot eliminate it entirely, but you can reduce it. Use "grounding" techniques where you provide the specific source material the AI must use to answer. Explicit instructions to "admit if you don't know" also help.

This is currently a legal grey area and varies by country. In the US, current rulings suggest that purely AI-generated work cannot be copyrighted, but work with significant human modification might be.

It is unlikely to replace entire roles but will replace specific tasks. Roles that rely heavily on repetitive creation or basic data synthesis will evolve. The most effective employees will be those who use GenAI to augment their capabilities.