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Top Questions to Ask Before Outsourcing AI Development

Before outsourcing AI development, ask key questions to ensure a successful partnership. First, inquire about their experience with AI development and request case studies or references. Ensure they have a proven track record in building AI agents relevant to your business. Ask about their approach to data security and privacy, especially when handling sensitive information. It's also crucial to understand how they align their work with your business needs and how they train and optimize models. Ask about the tools and frameworks they use, like TensorFlow or PyTorch, to ensure compatibility. Additionally, discuss post-development support and how they measure success and ROI to ensure the AI solution delivers real business value. These questions will help ensure you choose the right partner for creating an AI agent that benefits your business.

https://www.samyak.com/news-po....st/how-to-build-an-a

Building AI Agents That Create Real Business Value

Building AI agents that create real business value involves designing systems that enhance productivity, improve decision-making, and solve specific business challenges. Here’s how to build an AI agent that delivers meaningful value:

Identify Business Problems: Start by pinpointing the specific challenges your business faces. Whether it’s automating customer support, optimizing inventory, or enhancing marketing strategies, the AI agent should be tailored to solve a clear business problem.

Leverage the Right Data: Building an AI agent requires access to relevant data. Ensure that the data used for training is high-quality, up-to-date, and aligned with business goals to maximize effectiveness.

Select the Right AI Tools: Choose the right frameworks and tools, such as TensorFlow and PyTorch, to develop the agent. Depending on the task, consider integrating NLP, machine learning, or recommendation algorithms to achieve the desired outcomes.

Focus on ROI: The AI agent should deliver a clear return on investment (ROI). Whether it’s through cost savings, increased efficiency, or enhanced customer experiences, ensure the agent contributes directly to the bottom line.

https://www.samyak.com/news-po....st/how-to-build-an-a

Why AI Agents Should Not Act Alone
AI agents should not act alone due to several key reasons:
Lack of Ethical Judgment: AI agents rely on algorithms, not ethical reasoning. Without human oversight, they might make harmful or biased decisions.

Bias and Inaccurate Data: AI agents are only as good as the data they use. Inaccurate or biased data can lead to unfair outcomes, requiring human intervention for data accuracy.

Lack of Empathy: AI agents lack emotional intelligence and cannot understand human context or emotions, making human involvement essential for sensitive situations.

Accountability: AI agents cannot be held accountable for mistakes. Human oversight ensures responsibility, especially in high-stakes areas like healthcare or finance.

Complex Decision-Making: Building an AI agent for complex decisions is difficult. Humans are needed for judgment and creativity in scenarios requiring nuanced thinking.

In summary, AI agents should always have human guidance to ensure ethical, accurate, and empathetic decision-making.

https://www.samyak.com/news-po....st/how-to-build-an-a

What Tools AI Agents Use
When you set out to build an AI agent, the first step is understanding the core components required for its development. To create an AI agent, you must define its purpose, whether it’s for automating tasks, providing customer service, or analyzing data. Building an AI agent from scratch involves gathering the necessary tools, frameworks, and datasets. The process typically starts with choosing the right machine learning models, such as deep learning or reinforcement learning, depending on the complexity of the tasks your agent will perform.
If you're wondering how to build your own AI agent, a good starting point is to decide on the technologies you'll use, such as TensorFlow or PyTorch for training your model. These frameworks provide the flexibility needed for building an AI agent that can understand inputs and make decisions based on data. After training your model with the relevant data, you'll need to fine-tune it through testing and adjustments to ensure accuracy and efficiency.
Overall, building an AI agent requires a deep understanding of both the theoretical and practical aspects of AI technologies.

https://www.samyak.com/news-po....st/how-to-build-an-a

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When a Large Language Model Makes Sense
Building an AI agent from scratch can be an exciting and challenging project. If you're interested in creating an AI agent or learning how to build your own AI agent, it's important to understand the basic steps involved in the process. First, you'll need to define the purpose of your AI agent. This could range from automating simple tasks to providing more complex solutions such as natural language understanding or data processing. Next, you should choose the right tools and platforms that fit your needs, such as TensorFlow or PyTorch, which are widely used frameworks for developing AI models.
The next step in building an AI agent is to gather and prepare data. Data is crucial because it forms the foundation of the learning process. Depending on the type of agent you're creating, you may need a large dataset for training the model. If you’re creating a conversational agent, for example, a dataset of conversations or text data would be beneficial. After collecting the data, you will train your model using machine learning techniques, such as supervised learning, reinforcement learning, or unsupervised learning.
As you build an AI agent, it's important to constantly evaluate its performance. This means testing the agent with real-world data to see how it reacts and adjusts its behavior. This iterative process of training, testing, and refining is essential to ensure the agent performs well in diverse situations.

https://www.samyak.com/news-po....st/how-to-build-an-a

How Microsoft Azure Fabric Unifies Modern Data Ecosystems


Microsoft Azure Fabric simplifies enterprise analytics by unifying data engineering, data science, and business intelligence into one scalable ecosystem.

Traditional analytics stacks often operate in silos — separate storage, transformation, and reporting environments create inefficiencies. Microsoft Azure Fabric integrates lakehouse architecture, real-time analytics, data governance, and advanced reporting within a single platform.

This unified framework improves collaboration between data teams, accelerates insights, and reduces infrastructure complexity.

Modern enterprises need connected analytics environments — not fragmented tools.

Know more: https://predictaanalytics.io/microsoft-fabric/

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