Agentic AI Explained: Transforming Generalists To Specialists

AI, like large language models (LLMs), are already excellent at handling various tasks, from computing to writing essays. But here’s the thing—they’re generalists. The output can be good, but most feel too cookie-cutter. Agentic AI solves this issue.

What Is Agentic AI?

At its core, agentic AI is about assigning roles to AI systems. Instead of one AI trying to do everything, you create specialized agents with specific tasks, skills, or objectives.

These agents can then work independently or collaboratively, depending on the task. For example, one AI agent might be dedicated to data analysis and generating insights. Another could handle creative tasks like generating content or brainstorming ideas. The result? Each agent focuses on what it does best, producing more refined, efficient, and relevant output.

Why Does Agentic AI Matter?

Agentic AI workflows, even those using older AI models like GPT 3.5, can be on par or have better results than newer ones. Now, imagine newer models using agentic AI. You get:

Better Results, Faster

When AI agents have specific roles, they don’t waste resources figuring out tasks they weren’t designed for. A creative-writing agent doesn’t need to learn math, and a data-focused agent doesn’t need to be poetic. They stay in their lane, which means quicker, higher-quality results.

Holistic Problem-Solving

You get a well-rounded solution by having multiple agents tackle different parts of a problem. Think of it like a relay race. Each runner has their stretch, and together, they cross the finish line faster than one person sprinting.

Tailored Outputs

Generalized AI feels generic. Agentic AI ensures responses are tailored to a specific audience, industry, or task, making it more useful in specialized fields like healthcare, finance, or education.

Collaboration Made Simple

With agentic AI, multiple agents can interact with each other to complete complex tasks that would be overwhelming for a single system. For example, an AI agent writing an article can pull insights from another agent designed for research, creating a seamless back-and-forth process.

How to Train Agentic AI with AI Compute

Training agentic AI is about creating specialized AI systems that excel at specific tasks by leveraging the power of AI compute. Use cloud-based AI GPU compute and inference services like Tensorwave for cost-effective scalability. Then, do the following:

Define the Role of Each AI Agent

Start by identifying what tasks your AI agents will perform. For example, will an agent analyze market trends, detect fraud, or generate customer responses? Each agent’s role will determine the type of model and data you’ll need.

Gather and Prepare Data

Collect high-quality, task-specific data that the agent will need to learn from. This may include historical transaction records, customer interactions, or stock market trends. Preprocess the data by cleaning, normalizing, and labeling it for training.

Select a Pre-Trained Model

Choose a foundational model (like GPT, BERT, or domain-specific) as the starting point. These models already have general knowledge, saving time and compute resources compared to training from scratch.

Fine-Tune the Model with AI Compute

Use cloud-based AI compute platforms to fine-tune the pre-trained model on your domain-specific data. This step ensures the agent understands your business context, like recognizing specific fraud patterns or responding to customer queries in your company’s tone.

Optimize the Model for Inference

Once the model is fine-tuned, optimize it for deployment. This involves reducing its size (e.g., via quantization) and ensuring it runs efficiently in your preferred compute environment.

Deploy and Scale for Real-World Use

Deploy the trained agent using scalable AI compute resources to handle inference workloads. This ensures your agents can process real time data efficiently, whether serving customers or analyzing trends.

Real-World Use Cases for Agentic AI

Agentic AI with AI compute has an incredible range of use cases that unlock more intelligent, more efficient workflows across industries. Here are some of the most popular use cases.

Healthcare and Medicine

Agentic AI and AI compute improve processing data and generating insights. For example, personalized treatment plans benefit from a combination of agents working together.

• Data analysis agent processes patient histories and genomic data
• A predictive modeling agent forecasts disease risks
• A recommendation agent suggests tailored treatment plans
• A communication agent translates those findings into clear language for patients.

AI compute enables these agents to handle massive datasets like patient records, medical images, and real time drug interactions. GPU-based computing or cloud-based infrastructure supports intensive image recognition tasks, ensuring speed and accuracy.

Finance and Banking

The financial sector thrives on speed, accuracy, and security, making it an ideal space for agentic AI. Fraud detection relies on agents like a transaction monitoring agent to scan millions of transactions for irregularities, a risk assessment agent to evaluate flagged transactions, and a customer service agent to communicate findings to clients or teams.

Creative and Content Generation

Agentic AI helps you scale content velocity that isn’t robotic or generic. Depending on your content strategy, you can have an agent that creates content briefs.

Then, a research agent that uses tools like Beautiful Soup to scrape and consolidate website data. The data can be passed to a writer agent who uses scraped data as its knowledge base.

Finally, you can have another agent that ensures that the content written by the writer agent aligns with the content brief.

Customer Support and Service

Agentic AI provides fast, personalized assistance. In a 24/7 multichannel support system, a query classification agent identifies customer questions, a problem-solving agent provides solutions, an escalation agent flags issues requiring human intervention, and a feedback analysis agent gathers insights to improve future interactions.

Supply Chain and Logistics

Agentic AI simplifies supply chain management using specialized agents to handle complex tasks. A forecasting agent predicts demand with historical and real time data, a logistics agent optimizes shipping routes and schedules, and a monitoring agent tracks inventory to prevent shortages or overstocking.

In autonomous warehouses, robots and agents team up to pick, pack, and ship items. Advanced computing powers vision systems, task planning, and coordination algorithms, keeping operations smooth and efficient.

Key Takeaways

Agentic AI enhances how artificial intelligence functions by assigning specific roles to AI agents, creating a collaborative and specialized ecosystem. As a quick recap, here are some key takeaways you might’ve missed:

• Specialization Improves Efficiency: Agentic AI assigns specific tasks to agents to help them perform at peak efficiency without wasting resources on irrelevant processes.
• Enhanced Collaboration: Multiple agents can work together seamlessly, solving complex problems through specialized collaboration.
• Tailored Outputs Drive Results: Agentic AI creates more personalized and actionable solutions, crucial for industries like customer service and healthcare.
• Future-Ready Workflows: Whether predicting trends, detecting fraud, or streamlining supply chains, agentic AI provides faster, smarter, and more holistic solutions.
• AI Compute Enables Scalability: Cloud-based AI compute platforms are vital for training, fine-tuning, and deploying specialized agents to handle real time workloads.

Tensorwave lets you scale agentic AI workflows through cloud-based AI compute, training, and inference. Try it out today!