AI Agents & Angentic Workflow: Why, How and The Impact

But first of all, why are we so focused on developing AI agents and agentic workflows?

Among the many explanations, I find myself preferring Prof. Andrew Ng’s sum the most- it's not easy to make human intelligence cheap, but we can make it happen to artificial intelligence.

In today's world, human intelligence is one of the most valuable, also most costly resources. Accessing high-quality services, and intelligence has always come with a steep price tag - after all, training and supporting human intelligence is a costly endeavor, which is why healthcare, education, and specialized services require substantial resources to deliver.

That's where AI agents come in. While making human intelligence more affordable is a challenge, making AI affordable and accessible to more is entirely within reach.

So, what are exactly AI agents?

At their core, AI agents are autonomous digital “helpers” with a clear purpose. Unlike traditional automation, which just follows pre-set rules, AI agents can evaluate situations, adapt strategies, and make decisions to meet specific goals.

They’re not bound to strict instructions; instead, they operate within a framework that promotes learning and improvement, adjusting their actions as they go.

This guiding framework is known as an agentic workflow. Imagine it as a flexible roadmap for the agent.

Sounds like a description for automation? Well, yes and no.

Traditional automation is excellent for repetitive, predictable tasks, whereas agentic workflows are suited to complex, changing environments where decision-making, learning, and adaptability are essential.

In short, automation is about following rules, while agentic workflows are about achieving goals.

Real-World Examples of Agentic Workflows

A familiar example of agentic workflows in action is Retrieval Augmented Generation (RAG), something that may already be woven into the tools we use every day.

RAG lets AI search external sources—like databases, websites, or documents—for relevant information, improving the accuracy and relevance of its responses. Unlike traditional language models, which are limited to what they were originally trained on, RAG empowers an AI agent to pull in real-time information from outside sources before generating a response.

This ability to incorporate fresh, relevant information makes RAG incredibly useful in areas like healthcare and customer service, where accuracy is essential. With RAG, AI agents can deliver responses that aren’t just correct—they’re current and well-informed. It’s like having a knowledgeable assistant who never lets their info get outdated.

Another example of agentic workflows in action is in training AI models.

Agentic workflows don’t just make AI more effective; they can also help train the next generation of AI models. By letting models work iteratively, revising and reflecting on their outputs, agentic workflows produce high-quality data that can train future models. And because this approach is resource-friendly, it means we can build smarter AI without needing a supercomputer in every lab.

While training AI on AI-generated data can be controversial, agentic workflows make this process more precise. Using a structured workflow, AI can generate high-quality data that genuinely contributes to improving future models. Think of it like a self-improving feedback loop—a smarter, more efficient training process that gets better with each generation.

AI Agents Across Different Industries

AI Agents are making an impact across multiple fields. Here are two examples of how they’re driving progress:

• Healthcare: Smart Patient Monitoring

In healthcare, AI agents use real-time patient data to detect patterns, predict potential issues, and alert medical staff before anything goes wrong.

Imagine an AI agent monitoring a patient’s vitals. If it notices a sudden spike in blood pressure, it can alert doctors to step in immediately. By adjusting to each patient’s unique needs, these agents keep doctors one step ahead, making patient care more proactive.

• Finance: Autonomous Trading and Risk Management

In finance, where split-second decisions are crucial, AI agents monitor market trends, assess risks, and make trades in real-time without waiting for human input.

Let’s say the market suddenly dips. An AI agent can react instantly, selling off high-risk assets and rebalancing the portfolio. It’s like having a tireless, adaptable trader in your corner, working to keep investments steady—even in volatile conditions.

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Agentic workflows are bringing about a new age of intelligent autonomy. AI agents can now tackle complex challenges, respond to new data, and learn from their experiences—all on their own. This shift means AI isn’t just a tool; it’s becoming a true partner in overcoming challenges and driving meaningful progress.

Another term we may hear a lot is Agentic AI, which is neither the same with AI agents nor agentic workflow.

• Think of AI agents as the autonomous 'doers' in a system.
• Agentic AI is the overarching concept of AI behaving autonomously and goal-oriented, often involving multiple agents or agentic workflows.
• Agentic workflows provide the adaptive structure that enables AI agents to achieve their goals.

In short, AI agents operate within agentic workflows, and Agentic AI refers to the entire approach of autonomous, goal-driven AI systems.

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