Demystified: Hype around Virtual AI Agents!
What are AI Agents? Whats with the hype? Why they exist? Why should businesses care about Virtual AI Agents? What AI Agents are capable of?
Towards the end of 2024, we saw a significant shift from the buzz around “Generative AI” to the new trend - “Agentic AI”. This emerging paradigm has sparked various definitions, each attempting to encapsulate its essence. In this post, I aim to demystify Agentic AI drawing from my practical industry experience. I will dwelve into what AI Agents are, why they exist, why they are necessary and what they are capable of. Before embarking on this journey, let me first unravel the meaning behind the term "Agentic AI" and explore what it means for AI to possess agency.
As this definition suggests, Agency refers to the ability to act or intervene to achieve a specific outcome. AI Agents are precisely that; they act on behalf of the user to accomplish a defined objective or produce a desired result as specified by the user.
What are Virtual AI Agents?
Agents as fully autonomous systems powered by Generative AI that can reason, make decisions based on given objectives and take actions using various tools to accomplish complex tasks.
How are AI Agents different from standard computer algorithms and AI automated workflows?
Automation has always been a cornerstone of technological progress. Over time, industries and businesses have embraced various technologies to automate workflows, driving efficiencies and reducing costs. Each new technology introduced its own nuances in solving problems. We have evolved from monolithic code repositories to sophisticated algorithms powered by interconnected micro-services, and eventually to AI-powered workflows. So, how are AI Agents different? Why can’t we just rely on a set of algorithms to accomplish tasks? Let’s break it down:
Algorithms:
An algorithm is a predefined set of instructions designed to execute a specific task. It performs one task at a time and consistently produces the same output when given the same input. Static in nature, algorithms follow a singular, predetermined path and are unable to adapt dynamically.
AI-Powered Workflows:
These workflows take automation a step further by integrating AI model outputs. In AI-powered workflows, user inputs are processed in a structured, predefined manner to execute tasks. For example, they can predict user behavior, detect intent, or determine the next best action. However, these workflows remain rigid: they follow a sequence of predefined steps informed by AI outputs to accomplish a goal.
Consider the example of a travel booking assistant: If you ask your AI Travel Assistant to show you hotels in Tokyo under $300, the system would trigger a workflow. It might first detect your intent ("Find Hotels") and identify relevant filters ("under $300"), then guide you step-by-step to specify travel dates, the number of visitors, and hotel preferences. Finally, it would call APIs from hotel booking sites to retrieve and display results.
Please note, that such a flow is predesigned in tools like Google DialogFlow that do not have much of flexibility. For instance, what if you wanted to set a condition: “Book a hotel in the heart of the city only when the price drops below $300”? Traditional AI-powered workflows can’t handle such dynamic and evolving scenarios because they rely on rigid, predesigned flows, such as those created in tools like Google DialogFlow.
AI Agents:
AI Agents take automation to an entirely new level by introducing adaptability, reasoning, and dynamic decision-making. They are sophisticated systems capable of handling variations in user inputs, adapting continuously to new information, and producing acceptable outputs using a range of tools and functions. Unlike static workflows, AI Agents can execute instructions dynamically, gather missing inputs from the user, and make decisions autonomously to complete a task.
Returning to the travel example, imagine instructing your AI Travel Agent to:
“Book a flight to Japan for a family of three in April for two weeks. Plan an itinerary with visits to cultural landmarks and unique dining experiences.”
An AI Agent could dynamically interpret your instructions, interact with tools for travel planning, tour guides, and booking APIs, and plan the entire trip. It could monitor hotel prices, book accommodations when they meet your criteria, and even schedule dining reservations. Additionally, you could instruct the AI Agent to send you a summary for review before finalizing bookings, leaving you to simply approve payment.
How’s that for seamless, intelligent automation? AI Agents embody the future of actionable, context-aware, and autonomous systems that go beyond what algorithms and workflows can achieve.
So, in summary, here are the main notable differences between Algorithms, Workflows and AI Agents:
Mainly, AI Agents can learn from new data and continuously adjust their performance and behavior over time with ongoing instructions.
Algorithms and Workflows would always have one standard output based on the same set of inputs but AI Agents output may differ, adapting to the environment and dynamic situation.
An AI agent can act independently to achieve goals, whereas an algorithm requires explicit instructions and cannot initiate actions on its own.
In short, AI Workflows are systems - “Informed by AI, executed per Human instructions” where as AI Agents, are “informed by Humans, executed by AI”.
When should you build AI Agents?
Your first instinct in building the AI Agents should be to NOT build AI Agents! At least not right away! While Agentic AI systems excel at handling highly complex tasks, they often come with tradeoffs: higher costs, increased latency, and reduced control over how a specific task is executed.
For many well-defined tasks, AI-powered workflows can provide the predictability and consistency you need. Similarly, simpler use cases may only require single LLM calls combined with retrieval-augmented generation (RAG) and summarization tasks, eliminating the need for fully-fledged AI Agents.
AI Agents are best suited for scenarios where:
The goal is clear, but the path to solving the problem is complex and open-ended.
It’s impractical to predetermine all possible paths due to numerous permutations and combinations.
Before deploying an Agentic AI system, it’s crucial to establish a trusted environment within your organization. Confidence in the agent’s decision-making is vital, as these systems often operate with a degree of autonomy. To mitigate risks, implement robust guardrails to detect and address anomalies, ensuring that any errors are manageable and within an acceptable margin.
In summary, the decision to build AI Agents hinges on understanding the tradeoffs. Agents should be reserved for use cases where flexibility, adaptability, and model-driven decision-making are essential, and where the benefits outweigh the challenges.
What are some practical implementations and Use Cases for AI Agents?
AI Agents are rapidly finding applications across various industries, transforming how businesses operate. Here are some practical examples:
Customer Support:
AI Agents are revolutionizing customer support by either directly handling customer queries or assisting support agents for improved efficiency. For example:Service Management:
AI Agents are increasingly being used to manage end-to-end service desk processes, addressing user questions and resolving issues autonomously. For instance:ServiceNow ITSM AI Agent offers an intelligent solution for IT Service Management, streamlining operations and reducing response times.
Travel Booking:
Major travel companies are investing heavily in developing AI-powered Travel Agents. These systems aim to provide personalized, end-to-end travel planning and booking. Here’s a demo that highlights their potential.
Additionally, no discussion of AI Agents would be complete without mentioning the groundbreaking advancements from:
These incredible systems showcase the cutting edge of what AI Agents can achieve, setting the stage for a future where AI becomes an integral part of our daily lives.
Summary
AI Agents are poised to play a vital role in reshaping industries and enhancing our everyday experiences. I hope this post has introduced you to the exciting world of Virtual AI Agents and their transformative potential. I will continue to track the latest advancements and explore what they mean for businesses and individuals alike. Stay tuned!