If you thought the introduction of generative AI was revolutionary, we’re about to enter a whole new era. Agentic AI is here, and it’s set to transform how human beings and machines interact forever. Imagine an AI-powered agent that doesn’t just answer questions about how to create the perfect marketing strategy – it does the research, creates the content, and monitors the results for you.
Or how about an AI-powered solution that can optimize inventories on the fly, streamline your supply chain, and keep partners up to date? That’s the promise of agentic AI – bots that can complete a wider range of tasks autonomously, acting as an actual “member” of your enterprise team.
So, what exactly is agentic AI? How does it work, and what are the opportunities offered by this new generation of artificial intelligence? Here’s everything you need to know.
What is Agentic AI?
Agentic AI, otherwise referred to as “autonomous AI”, are advanced systems that can make decisions and take multiple actions on their own, to achieve certain goals. They adapt in real-time, solve multi-step problems, and use sophisticated reasoning to minimize the need for human intervention.
Traditional AI models and generative AI bots focus on completing individual tasks. They respond to prompts or do specific things – like creating content for a social media post. But these tools are ultimately “reactive” – they respond to human beings and what we tell them to do.
Agentic AI still needs to be “programmed”, but it’s more proactive. Agentic bots can ingest and analyze vast volumes of data from multiple sources to independently detect challenges, develop strategies, and execute problem-solving tasks.
They use “chaining” capabilities to take a sequence of actions in response to a single request – breaking down the process of getting to an end goal into various steps. For instance, if you asked an agentic AI tool to build a website it would potentially:
- Develop a website structure and set of wireframes
- Create content for each critical page
- Write the necessary backend code with HTML, CSS, and Java
- Design and incorporate visuals and graphics
- Test the system for bugs and responsiveness
How does Agentic AI Work?
To achieve a new level of autonomous decision-making, agentic AI systems rely on a wide range of different technologies, from machine learning, to autonomation technologies, natural language processing, and more. While many agentic AI systems harness similar creative abilities to generative AI models, like ChatGPT, they’re different in various ways.
They’re focused on making decisions and “executing” tasks, more than creating content, and they don’t rely as heavily on specific human prompts. Instead, they’re set to achieve specific goals or objectives, like maximizing sales or improving customer experiences.
Most agentic AI solutions use a four-step process to solve problems:
- Perception: First, the AI agent gathers and processes data from various sources (databases, sensors, digital interfaces, etc.), collecting the information required to complete a task.
- Reasoning: Next, an LLM acts as a reasoning engine, helping the agent to understand tasks, generate solutions, and coordinate specific models for certain functions (such as visual processing or content creation). This step often involves techniques like retrieval-augmented generation (RAG) to enable access to proprietary data sources.
- Acting: Integrating with external tools through APIs, the agentic AI system quickly executes tasks based on the plans it created. Guardrails are often built into these agents to ensure they execute these tasks safely and correctly. For instance, these guardrails might ensure an AI customer service agents gets approval from a human before granting a refund.
- Learning: Finally, agentic AI learns and improves through a feedback loop, allowing it to become more effective at decision-making over time.
The Benefits of Agentic AI Technology
Across industries, generative AI and GPTs are transforming organizations by streamlining tasks, and improving data analysis. Agentic AI will build on this potential, enabling access to diverse data through accelerated query engines and RAG. Additionally, they’ll enable companies to automate a wider range of tasks with more precision.
With super-charged reasoning and execution features, agentic AI will enhance various aspects of work previously less suited to AI automation, such as dynamically reconfiguring supply chains. Agentic AI technologies will offer:
Improved Specialization
Most previous AI solutions have been designed to perform relatively generic tasks, like creating content for a website. Agentic AI can be designed to “specialize” in specific processes, carrying out very granular tasks, like updating a supply chain based on weather conditions.
Additionally, companies can create multiple agentic “roles” quickly for different focused tasks. For instance, agents could be created to enable information, analysis, workflow generation, and employee assistance for sales and marketing teams – all working together behind the scenes.
Improved Reach and Integrations
LLMs are trained on static datasets which need to be regularly updated to include new information. They don’t go out and gather new information from the web after they’re trained without human intervention. They also can’t autonomously monitor systems for new data updates.
Agentic AI, on the other hand, can be designed to autonomously search the web and query databases with APIs (application programming interfaces). They can collect information in real-time, and manage tasks like real-time monitoring, data logging, and trend analysis. This means they can constantly refine their decision-making process on their own.
Flexibility and Precision
For content creation, LLMs are excellent at processing and generating human-standard text. They allow humans to interact with bots using natural language, rather than programming code. However, they’re not ideal for tasks that require higher levels of precision, repeatability, and verifiability – that’s where traditional programming comes in.
Agentic AI offers the best of both worlds. It can use LLMs to handle tasks that require dynamic, flexible responses. However, it can also leverage traditional programming to follow strict rules and adhere to specific logic. This enables the AI to be both innovative and intuitive, as well as precise and reliable.
Greater Autonomy
Obviously, one of the biggest benefits of agentic AI is that it requires minimal human input. Agentic AI tools can operate independently without the need for constant prompts and human oversight. This enables streamlined operation in environments where human supervision might not always be needed.
For instance, you could create an AI agent to manage a marketing campaign, monitoring its performance and adjusting responses based on feedback. In cybersecurity, agents could constantly monitor network traffic and system logs for anomalies, and take proactive strategies to reduce risks. In supply chains and manufacturing, AI can autonomously place orders with suppliers and adjust production schedules based on changing market dynamics.
Proactive Innovation
As mentioned above, one of the great things about agentic AI is it’s more “proactive”. It doesn’t just wait to be told what to do, it identifies a specific goals, and works alone to help companies achieve that goal. With their enhanced judgement capabilities, agentic AI tools are fantastic at experimenting with different concepts and driving innovation.
One great example is the “ChemCrow”, agent created for a chemistry team, which has been used to plan and synthesize new insect repellent products. With these abilities, agentic AI can open the door to rapid problem solving and product creation in a more proactive – intuitive way.
Simplicity
Various business functions currently performed using software products could be replaced or enhanced by Agentic AI technology, improving workplace efficiency and productivity. For instance, think about the ticketing systems software developers use to track project progress.
Usually, these systems require a lot of tables, tabs, and workflows that can be difficult to navigate. When human beings need to find the right data, they need to search through multiple menus and environments. With agentic AI, users could simply ask for the information they need in plain language, streamlining the path to success.
Greater Trustworthiness
Since Agentic AI systems have better cognitive reasoning capabilities than other forms of AI bots, they’re less likely to suffer from problems like “AI hallucinations”. They’re also a lot better at sifting through information and validating its quality and reliability. This makes the decisions these systems make a lot more trustworthy and explainable.
For instance, an agent designed to support customer service teams that needed to access data about a customer could immediately identify that the most “reliable” data is likely to be located in a company’s CRM system – rather than in an email, or a spreadsheet.
Agentic AI Use Cases: AI Agents in Action
Since Agentic AI has a huge range of benefits to offer, it makes sense that there are a lot of use cases for this technology across industries too. On a broad scale, Agentic AI can help with all forms of business operations across sectors. It can handle things like supply chain management, optimizing inventory levels, forecasting demand, and logistics planning.
In the human resources landscape, AI agents could even transform talent management by automating various processes such as screening candidates, scheduling interviews, and coordinating training. They could even be used to create personalized development plans for each team member.
Beyond that, Agentic AI can support:
Customer Service Teams
Generative and conversational AI tools are already having a massive impact on customer support, enhancing self-service capabilities and routine communications. Agentic AI will bring a new level of capacity to AI support agents, enabling them to troubleshoot issues for customers, and complete a wider range of tasks, like initiating refunds, or entering a support ticket into a system.
Manufacturing
In manufacturing, Agentic AI will have a huge range of applications. It will be able to control and improve the flow of production lines, make suggestions on how to improve product design, and even track the performance of machines with IoT sensors.
It’ll even be able to predict wear and tear and production outages, helping companies to avoid unscheduled downtime and the associated costs. Already, companies like Juna.AI are using AI agents to run virtual factors in a way that enhances production speed and reduces energy consumption.
Sales
For sales agents, the process of finding and nurturing leads can be time-consuming and complex. Agentic AI solutions could help to make sales teams more efficient and productive. For instance, Salesforce is already introducing agentic AI tools that can help sales teams interpret customer messages and recommend follow-up actions or book meetings.
There are even agentic AI coaches available for sales teams that can provide personalized feedback to human agents, as well as specialist tools for sourcing and identifying prospects. For instance, HubSpot’s Breeze solutions offer tools for identifying and validating prospects in sales.
Healthcare
With their ability to adapt rapidly to different environments, interpret human emotions, and even show empathy, agentic AI systems are great for healthcare environments. Recently, an agentic AI healthcare company, Hippocratic AI introduced a range of agents that offer different levels of healthcare and social support to people with varying care needs.
Agentic AI solutions can also plan treatment plans for patients based on discoveries and tests, follow up with individuals about treatment plans, and even track vitals on behalf of doctors.
Marketing
Agentic AI is a powerhouse tool for marketing teams, going beyond the capabilities of current generative AI systems. Already, generative AI has the potential to save marketers an average of three hours on each piece of content they create. Agentic AI will be able to further streamline marketing tasks, by tracking the outcomes of campaigns and adjusting them automatically.
They’ll also be able to create and publish content at specific times, respond to comments that customers post on social media and other channels, and suggest campaign improvement strategies.
Software Engineering and Development
AI agents are boosting the productivity and performance of developers by automating all kinds of repetitive coding tasks. Some experts even believe that AI will be able to automate up to 30% of the working hours for software developers by 2030.
AI agents can generate code, manage entire development lifecycles, design system architecture, and even oversee quality assurance processes. This could accelerate the pace at which new software and applications reach the market.
Cybersecurity
As digital threats continue to evolve, particularly thanks to the rise of deepfakes created with generative AI, many companies are beginning to rely on advanced systems to mitigate risks. In this space, Agentic AI could act as a tireless guardian of business data and networks.
They could be used to autonomously monitor traffic in a network or environment, detect anomalies, and proactively respond to threats in real-time, all without waiting for human oversight. This could free up human experts to focus on more complex security challenges.
Finance
In the world of trading, investment, and banking, Agentic AI could play a number of roles. It could assist with revolutionizing portfolio management, analyzing market trends and making split-second decisions based on opportunity and risk.
Agentic AI tools could even dynamically adjust investment strategies for individuals and larger investors based on news events and real-time economic data. This could lead to the development of more efficient, thriving markets.
Agentic AI: The Challenges and Considerations
While the potential of Agentic AI is exciting, there are still a lot of challenges to overcome – particularly in the realm of ethical AI development. First, there’s the question of how safe it is to give AI tools full decision-making power, and the ability to operate constantly without human oversight.
If AI models aren’t fully aligned with human values and expectations, they could make decisions that eventually harm human beings. Additionally, while the decision-making processes of Agentic AI tools can be easier to understand in some environments, there’s still a risk of “black box AI”.
The more complex an Agentic AI system becomes, the harder it is to explain how it reached a specific conclusion. This raises questions regarding accountability, making it important to determine who is actually responsible when an AI system makes a mistake.
At the same time, there’s the potential impact on the job market to consider. While Agentic AI could improve productivity and create new opportunities in certain industries, it may lead to the displacement of certain roles. At the very least, it will necessitate a shift in the kind of skills workers need, and the education they’re given.
Beyond all that, there are data privacy and security concerns to think about. As these tools become more autonomous, and deal with more sensitive information, robust safeguards will be essential to minimize the risk of breaches and data misuse.
Getting Started with Agentic AI: The Evolving Options
Agentic AI is becoming increasingly accessible, as more innovators in the AI market continue to experiment with their own intelligent systems. Google recently announced the arrival of Gemini 2.0, which will give developers more advanced Agentic AI capabilities within platforms like Vertex AI, allowing them to create their own autonomous agents.
Microsoft is experimenting with Agentic AI too, enabling access to a wide range of pre-built agents for various tasks, such as employee assistance, customer service, and sales. Companies can customize these pre-built agents within Copilot Studio, and even build their own autonomous agents from scratch, using a range of triggers and connectors.
Amazon has the AWS Bedrock ecosystem, where developers can access multiple models, as well as frameworks, guardrails, and other tools, to develop agentic AI solutions that are grounded with specific business data. Elsewhere, NVIDIA has introduced its “NVIDIA Blueprints” solution, giving developers reference code, sample data, demo applications and documentation to experiment with.
NVIDIA is even partnering with other industry leaders, like Accenture, to empower companies to rapidly implement Agentic AI into their workflows.
Embracing the Future of Agentic AI
Agentic AI is undoubtedly the next era of AI innovation in the enterprise. It marks a significant step forward from the days when AI solutions could only handle single tasks and processes. In the years ahead, Agentic AI will introduce a future where more tasks can be automated, streamlined, and improved by technology.
Of course, there are challenges to overcome here, from determining how much human work bots should be able to automate, to ensuring they adhere to human values and security guidelines. However, if we can master the future of Agentic AI, the opportunities are phenomenal.
In the years ahead, human beings could become more productive, innovative, and creative, with AI agents by their side. The key to success will be developing a strategy that keeps human beings in the loop when necessary, and focuses on ethical AI usage.
