The transition from Generative AI to autonomous AI agents is so fast and for all good reasons. With autonomy as a significant capability, AI agents can perform most tasks from start to finish without human supervision. What was previously so difficult to attain, outside of simply providing answers to FAQ questions, AI agents have now transformed how businesses manage their processes and rightly, from simple to complex.

According to Gartner, 80% of repetitive customer support issues will be resolved autonomously. Businesses see a massive opportunity in this. 

More surprisingly, given its huge potential, solution providers undergo a massive makeover and rebrand themselves as pure-play Agentic AI solution providers. There you are, and you find logistics AI agents, payment processing AI agents, and AI agents for customer support, so on and so forth. AI agents are doing more in less time, nudging you to try it out in no time. 

So, if you want to automate support or any other business processes, you need to understand the basics of autonomous AI agents. Let’s dive deep to explore everything from autonomous agents to their types, benefits, and best practices, including how pagergpt helps you harness the power of autonomous AI agents.

What do you understand by autonomous? It refers to having the power to self-govern, which further means having the freedom to make decisions and act independently.

Similarly, autonomous AI agents are autonomous entities or computer software programs powered by large language models (LLMs) with tooling capabilities that can reference data, reason in multi-turn interactions, make decisions, and perform actions to reach a goal. 

Let’s assume it is mandatory for your customers to change their apps’ passwords every three months. Built-in password AI agents would notify your customers and automatically help them update their app passwords for an extra layer of security. During the course, if there are any edge cases or the authentication flow fails, AI agents can suggest an alternative authentication method.

This gives a clear understanding that AI agents can self-learn to adapt to changing situations. They train themselves by mimicking chat histories or actions performed during previous calls. 

The advantage is that you can always expect smart and proactive answers without the need for human intervention in certain tasks, such as customer support. 

Let’s discover how they work.

The fundamental blocks that power autonomous AI agents are machine learning, natural language processing, and data analytics abilities. Here is how they proceed with each step. 

• Perception and data collection: AI agents initiate tasks by retrieving data from a wide range of sources, including customer purchase histories, APIs, and the company’s knowledge bases, to understand the context and intent and make informed decisions.

• Decision-making: After collecting data, AI agents utilize machine learning algorithms to identify patterns and predict outcomes. This helps them make decisions that align with their desired goals. 

• Action execution: Once goals are finalized through decision-making, AI agents are prepared to perform tasks based on queries. Various task executions include answering customer questions, fulfilling orders, initiating refunds, and transferring calls to service desk agents. 

• Learning and adaptation: The veracity of answers depends on how AI agents can update their inner learnings and continue to self-learn. The ability of AI agents to learn from previous experiences in conjunction with current interactions helps them stay informed and update their knowledge bases. This helps you generate the correct responses and avoid hallucinations. 

Notably, AI agents can perform well and become efficient only when they are trained with quality data. This also helps them improve decision-making and adapt to a wide range of tasks.

A quick overview of agentic AI vs. non-agentic AI chatbots 

Agentic AI has already been discussed. They are autonomous and can reason to act accordingly by making decisions. They can gain autonomy and human-like agency because they can,

• Connect with tools, APIs, and other systems 

• Refer to LLMs and produce outputs to match prompts 

• Use memory across past interactions and actions taken to train continuously and retrieve knowledge 

These are unique phenomena of AI agents that help them complete a set of objectives or tasks. 

A typical, or more specifically, non-agentic AI chatbot, on the other hand, follows a rule-based approach or predefined FAQs to answer repetitive questions. These chatbots possess limited adaptability, independent decision-making capabilities, and reasoning to guide users through complex journeys. They can only handle straightforward questions. 

Let’s refer to the same example of the password expiry. A non-agentic AI chatbot suggests a link to reset the password. For the worst-case scenario, if the link doesn’t work, the bot fails to guide. In most cases, it struggles to hand off properly or ends the chat. 

Non-agentic AI chatbots are restrictive and don’t level up with agentic AI chatbots. 

While autonomous AI agents can offer personalized outcomes based on training and make informed decisions, foundation models are best suited for generating answers, summarizing a draft, creating new emails, and other tasks related to content generation. On top of it, you can use foundation models like Amazon Nova, fine-tune them with your own data, and use them for one of two use cases. Most interestingly, foundation models can be used to build your AI agents. 

In essence, autonomous AI agents can understand goals in every multi-turn interaction and complete a task end-to-end. Foundation models help with content generation with multimodal capabilities. 

Let’s get a very concise comparison,

Now that you learn that autonomous AI agents are powerful, intelligent, and scalable for your mundane and repetitive processes, learn their various types too. Understanding their types helps utilize the right set of tools and utilize their decision-making abilities to meet your custom needs. Here they are,

Simple reflex agents 

This is the most basic type of autonomous AI agent. Simple reflex AI agents follow only if-then logic and make decisions based on current situations without referring to past experiences or memory. 

Example: 

The traffic light keeps the RED light on once cars are detected, not based on the traffic history. 

Model-based AI agents 

These agents refer to their internal models to observe and understand their environment, enabling them to plan, make decisions, and act accordingly. It can be like a person who perceives their surroundings, understands the situation, and executes a plan. 

Example: 

A notable example is a self-driving car. The model-based AI agents in the car utilize models to map their surroundings, including cars, pedestrians, and other vehicles, to predict situations and drive safely. 

Goal-based AI agents 

These agents drive towards specific objectives. They study the outcomes of their actions and select the appropriate one to achieve their goal, even if the situation changes. 

Example: 

An email-sorting email assistant organizes emails based on priority. It observes which emails are essential and organizes them in the right folder. 

Utility-based agents

As the name suggests, you can define utility-based agents as AI software programs that make decisions based on how actions would appear beneficial or unleash the most value, and then execute the tasks. 

Example: 

Let’s talk about recommendation engines or AI agents, which can suggest the best product a customer can buy based on their pricing preferences, search history, and ratings. 

Learning agents

AI agents that learn continuously by observing user interactions and the previously produced outputs to improve and make decisions are learning agents. They possess advanced adaptability and learn without any programming. 

Example: 

Consider an edtech learning app, for example, as an illustration of learning AI agents that can suggest the best lessons or courses tailored to a student’s adaptability and progress. 

Hierarchical AI agents

Hierarchical agents break down tasks into smaller chunks and distribute them among AI agents equipped to handle various levels of complexity, while simultaneously meeting the objective.  

Example: 

Let’s take customer support. Using a hierarchical AI agentic model, you can streamline everything automatically. A ticket will be assigned to the appropriate AI agent first for triage. It is then 

Multi-agent systems 

Multi-agent systems refer to the combination of multiple AI bots working together to achieve a common goal. They tackle complex queries and problems that a single-agent system cannot solve rapidly. 

Example: 

For travel booking, you can have one AI agent to book flights, another for hotels, and a third to create a plan for various activities. 

Additional examples include pagergpt, which enables you to create agents and sub-agents to streamline customer interactions and manage support seamlessly without any friction. 

These various types of autonomous AI agents are dynamic. So, you can pick the one that you believe can be tailored to your specific business processes. But understanding their key features keeps you ahead of the curve for a better ROI value. 

AI agents can vary depending on their abilities to perform a wide range of tasks. Key features determine how agents can become specialized, or something with limited abilities. Explore them so that it becomes easy to focus on the features while selecting AI agents. 

• Autonomy: It is a phenomenon that enables AI agents to become independent and execute tasks with minimal supervision.  Let’s say a customer stops visiting your ecommerce website. The AI agent observes this and communicates with the customer through personalized messages, such as an exciting offer or a prompt that encourages immediate action. 

• Reasoning/Adaptability: The adaptability of AI agents to changing situations is a crucial feature that enables them to navigate uncertainties and respond appropriately, much like a self-driving car using the most effective route when traffic on a certain route obstructs movement. 

• Tooling capabilities: AI agents can interact with your stack of tools and reference them while executing a task to achieve a specific goal.  For example, if an AI agent is tasked with sending follow-up emails after a sales call, it can connect with an ESP system to manage and streamline the workflow. 

• Multimodality: Regardless of the content type—text, image, video, or audio —AI agents exhibit multimodal perception to understand context and make informed decisions. 

•  Memory usage: This is an essential feature that enables AI agents to become exceptionally intelligent, as they can recall past experiences to establish a connection with the current situation and perform tasks. Built-in memory and access to third-party tools help improve decision-making and efficiency. 

• Action plans: AI agents get action plans that guide them with resources, constraints, and challenges. These plans help them follow the guidelines and execute tasks as required. 

• Learning techniques: AI agents primarily employ reinforcement and unsupervised learning methodologies to continually learn and enhance their efficiency. The most effective is reinforcement learning, in which AI agents can improve themselves by receiving continuous feedback. 

These are essential features of AI agents, which allow you to maximize their potential and drive the most value.

When autonomous AI agents can handle tasks independently, the benefits are wide. Your business processes are automated, enabling your team to focus more on addressing pending tickets. The benefits include, 

Efficiency and productivity

AI agents can boost productivity by 66%, says Nielsen Norman Group Research. So, forget the time spent on manual customer queries over a week. Autonomous AI agents automate mundane tasks, such as queries, content generation, or coding, thereby boosting productivity and efficiency. 

Scalability and adaptability 

Autonomous AI agents are not limited to rigid workflows. They can dynamically adapt to changes in queries and manage tasks outside the normal threshold. The Microsoft Blog states that autonomous agents can enhance teams’ performance by managing volume more efficiently. 

Modularity and expandability 

API-first designs and the modularity of AI agents eliminate the need to rebuild the entire framework to fit various use cases. An AI agent designed for customer support can be easily expanded to handle HR support, logistics, invoice processing, and any custom AI workflows. 

End-to-end automation 

Not only could you expect your AI agents to give answers to FAQs. They are designed to manage the entire workflow until a solution is provided—be it for PO for supply chains, payments, approvals, etc. 

Seamless tool integrations 

The API-first design of AI agents enables you to keep your tools in sync. As you create integrated systems, you can easily automate workflows and boost process efficiency. 

Around-the-clock availability 

Don’t worry about downtime. Once you deploy them, they operate 24/7, providing answers to queries or handling tasks behind the scenes. For example, AI agents notify of potential risks to apps when nobody is actually watching. 

Faster time-to-value 

Autonomous AI agents can self-train, eliminating the need for periodic retraining of models, as is required with traditional tools. You can custom-create AI workflows and deploy them faster to realize value. 

Safety and risk mitigation 

With the ability to detect anomalies in account activities or other business systems, AI agents instantly take preliminary steps to mitigate risks, allowing teams to take a data-driven approach to minimize the impact. 

Improved accuracy and compliance 

Autonomous AI agents are trained to comply with legal frameworks to help you protect customer data and build trust with accurate and context-aware responses and actions.

Just name what role you want autonomous AI agents to play they are ready to adapt. 

You can use AI agents for anything. There are numerous examples of AI agents that automate mundane and repetitive tasks, streamlining steps for greater efficiency and an enhanced user experience.  

A logistics AI agent can monitor the dynamic pricing structure in the marketplace and then recommend the best quote for carrier selection. Let’s consider an AI writing agent that can draft follow-up emails and help progress the deal. 

Here is a list of some common examples of AI agent use cases and applications. 

Autonomous AI agent for customer support 

The time to frustrate your customers with limited context-aware answers is gone. There are no longer repeated responses that say ‘Sorry, I am still learning. Can you rephrase your query?’ 

Autonomous AI agents can do more for your customer support. 

• Handling of complex queries: AI agents provide customized answers to unique and complex questions, and can even handle multi-step queries within a single conversation without losing context. 

• Personalized interactions: Autonomous customer service agents with fallback support understand where they lack the ability to fully satisfy a user’s question. It immediately transfers a call to the right person and helps resolve the problem rapidly. 

• Proactive support: AI agents are the ideal companion for your business, keeping your customers engaged with proactive support. Let’s say sending reminders for upcoming meetings or the next subscription payment nudges your customers to take the appropriate actions. 

• Consistent support quality: Your customers receive support across multiple channels, including email, web chat, Slack, Teams, intranet, voice, and more, without any friction. AI agents maintain consistency with accurate and relevant answers. 

Autonomous AI agent for sales 

Your sales team handles a multitude of tasks. However, things can go wrong if someone overseeing a new product call doesn’t show up. Thanks to AI agents. They can help you in many ways. 

• Lead generation AI assistant: When deployed on a website with a specialized program to monitor specific activities that qualify as essential triggers for lead prospects, the AI agent can send a message and build connections. And your sales team can save time on cold leads. 

• Follow-up AI agent assistant: Cut all those manual back-and-forth follow-up emails that your sales team sends. AI agents engage with high-potential prospects through personalized emails, seamlessly advancing the process without missing a beat. 

• Meeting scheduler AI agent: Harness the power of AI agents to automatically schedule meetings with prospects or set up a demo call without human assistance. No more forgetting to schedule a meeting. 

Autonomous AI agent for customer success 

Utilize AI agents to manage the critical aspects of customer success. Here’s how,

• 24/7 AI assistant: Your customers can have questions about the product at any time. But does your team remain active every time? Let your AI agent handle FAQs or custom queries accurately. 

• Customer onboarding assistant: Your SaaS product has a new customer. You have your AI agent to help him set up the account and walk him through every step of the essential touchpoints. 

• Churn risk detector: What you can miss easily, your AI agent can’t miss if any customer stops interacting with your brand. They notify the team to mitigate risks.

Autonomous AI agent for ecommerce 

Your ecommerce customers need extra help. Autonomous AI agents are tirelessly helping them in many ways ——

• Order tracking: Let your customers know about the status of their orders. AI agents always keep them on track with the latest update. 

• Product recommendation: Isn’t it amazing to have AI agents recommend personalized offerings and boost product sales? AI agents can do it for you based on customers’ purchase history, trends, and other relevant factors. 

• Customer query resolution: When can I expect to receive my product? What is the return policy? Can a discounted product still qualify for a refund? There are so many questions. You have autonomous AI agents to answer them all. 

Autonomous AI agent for event or hotel booking 

A dozen questions may perplex your customers regarding hotel or event-related activities. Autonomous AI agents are becoming essential to handle them. 

• Room booking: Autonomous AI agents provide clear answers about prices, available rooms, handle exceptions, and complete the booking process. All without human involvement. 

• Cancellations and rescheduling: Handle unpredictable activities as your customers request to cancel or reschedule a booking at short notice. 

• Round-the-clock guest queries: Have your support team manage high-impact guest queries, and delegate mundane queries to the smart, autonomous AI agents. Easily help them find answers to pet policy, breakfast options, and check-in times, etc. 

Autonomous AI agent for employee support 

Employees quickly scale their bandwidth when they can do more with less effort. AI agents guide them the right way.  

• Developer co-pilots: Always steadily come up with new features and functionality without any friction. AI agents handle the heavy lifting for DevOps, spanning from coding to debugging and documentation. 

• Onboarding assistant: Seamlessly manage everything for a new hire's onboarding process. Automatically set up accounts, provision software access, schedule a training, and so much more with autonomous AI agents. 

• IT helpdesk agent: Employees have productive days at work every day, as AI agents handle IT issues such as account unlocks, password resets, and software updates. 

• Internal knowledge AI agent: Every piece of information in real-time matters. AI agents retrieve information across various data sources for your people and empower them to solve their problems. 

Examples of applications of autonomous AI agents can go beyond these illustrated options. You can expand and create custom use cases tailored to your business needs.

Implementing and deploying AI agents requires the right approach and strategy. You can build it or buy it, too. 

The former option can be so seamless with a no-code AI agent builder platform. It provides the best way to utilize your existing tech stack, create AI workflows to automate their processes, and reduce manual workloads. 

Let’s get started on how you could use your resources with the available LLM agent frameworks. 

Here’s a general roadmap to get started. 

Define the purpose of your autonomous AI agents

Why do you want to create and deploy your AI agents? This should be your first question, including others. Stack your questions, 

• Do I need custom AI agents for complex customer queries?

• Are customer onboarding making the sales teams lose time?

• Why do return queries grow in high volumes?

Set a clear goal before you jump so that you can stay focused. 

Collect and prepare data 

Data is key to training your AI agents and equipping them to deliver accurate and context-aware answers. 

• Collect data for your specific purpose, such as a customer support AI agent. Customer chat histories, ticket log records, business policies, and other relevant information can be valuable sources of data. 

• Also, ensure your resources contain correct information to enhance response accuracy. 

Start with a pilot use case 

It is always ideal to start with something that makes sense to test and trial the effectiveness of the autonomous AI agent. It helps you avoid unnecessary iterations, high costs, and time. 

• Choose a high-impact use case. For example, a lead generation AI agent. 

• Create only a few important conversation flows to identify value and impact. 

This strategy is effective in satisfying buy-in, accelerating approvals, and fostering adoption. 

Choose the right enterprise autonomous AI agent builder 

Decide on how much resource you are ready to spend on building AI agents.

• Platforms like Amazon Nova require a significant amount of effort with coding to fine-tune and custom-build your agentic AI bots. 

• A 100% no-code platform like pagergpt is effortless to build AI agents, integrate with any tech stack, and implement custom workflows for any use cases. 

Make the informed choice that easily enhances faster time to market. 

Build the AI agent 

If you want to get started quickly, a no-code platform is an ideal choice. Start by—

• Choosing a pre-built template 

• Uploading your knowledge bases 

• Using pre-built integrations 

• Setting up AI actions that your agent will perform 

It’s that easy with a platform that comes with pre-built templates or allows you to create from scratch with its plug-and-play orchestrator framework.

Test and refine 

How do your workflows work? Do they align with the guidelines you have set up? All aspects, including those of others, can be clarified through testing and refinement. 

• Run the test with related questions.

• Verify if it can comprehend the user's intent.

• Verify the accuracy of responses.

• Use a feedback loop.

• Fine-tune its performances.

Always remember to execute frequent tests to maintain accuracy and relevancy. 

Deploy and monitor 

If everything works as expected and your business goals are met, it is time to deploy your AI agent. 

• Deploy where your customers are and where they love to connect. 

• You can choose your business website, as well as social media channels, including WhatsApp, Instagram, Messenger, and collaboration channels, among others. Also

• Also, prioritize bot analytics to monitor and improve bot performance.

To ensure that you capture the best ROI value for autonomous AI agents, it is essential to follow best practices for implementation. 

Just building and forgetting, thinking everything is just fine, never yields expected results. Many would start, but pause midway through the journey due to the overwhelming aspects associated with the project pipeline. 

Let’s understand what you should do,

Start small and then expand 

Select one use case at a time for your AI agent. Monitor how it works efficiently to solve the problems for a small group of users. If you achieve success, expand to other key areas and create workflows for your AI agents. 

Prioritize data quality 

AI agents can produce what they get. If you have incomplete, biased, or incorrect data, it will lead to flawed decisions and the incorrect execution of tasks that were not intended to be executed. Use a diverse set of high-quality data. 

Utilize RAG 

Using advanced Retrieval Augmented Generation is the best way to allow AI agents to retrieve answers from company data sources grounded in LLMs. 

This allows you to eliminate the chances of hallucinations and offer relevant and accurate answers. 

Preferably, you can offer agentic RAG-based self-service to your customers, allowing them to resolve their problems without the risks of hallucinations. 

Implement error handling and fallback support

Agents can encounter unusual scenarios that fall outside the scope of their training guidelines. They must be equipped to handle errors effectively. Ensure you have fallback mechanisms in place to let AI agents reroute tasks to human agents. 

Ensure compliance standards 

Compliance is of high importance with legal frameworks such as GDPR, HIPAA, SOC II, ISO/IEC, and many others to protect users' identities and build trust. 

Monitor KPIs for continuous improvement 

Unless you set clear KPIs and monitor them closely, it is challenging to effectively manage your support and drive success. Use support analytics and track KPIs like response time, query resolved, query unresolved, etc. These insights are valuable resources for improving performance. 

Keep humans in the loop 

Autonomy is fundamental in AI agents – but that shouldn’t make you feel that you no longer need human supervision. Ensuring human oversight of AI agents’ actions helps you build accountability, minimize risks, and offer a performance boost.

One can imagine that AI agents are superpowers and they can never put you at risk. However, they have certain limitations and can also cause harm. 

Hallucinations 

Making up responses is quite common for AI agents, which may be due to limited resources or a lack of understanding. Therefore, when tasks require deep comprehension, AI agents struggle and sometimes hallucinate. 

Putting appropriate guardrails prevents this phenomenon. Advanced RAG models, including human oversight, promote accuracy for autonomous AI agents. 

Compliance risks

It is mandatory to meet compliance requirements for GDPR, SOC 2, HIPAA, and other relevant standards. 

Lack of knowledge in handling user data leads to non-compliance with these legal frameworks and the delivery of unauthorized responses. You risk driving penalties. 

Choose to build with an AI agent platform compliant with all necessary security frameworks. 

Explainability 

AI explainability is crucial for understanding what AI agents do and why they do it. If you do not prioritize explainability for your AI agents, you cannot build trust. 

Ensure explainability to comply with regulations. 

• You can add explanations to describe how AI models make decisions and perform certain tasks. 

• Show every step AI agents take to process information and execute a task. 

• Ongoing resources

Maintaining AI agents requires significant ongoing resources. But if you build and forget it, agentic AI performance will deteriorate,  

The best thing to minimize spending on ongoing maintenance is to build with a no-code platform. Only create exceptional brand experiences, and let the platform builder handle the maintenance stuff.

Automating multi-step interactions or workflows yields high returns for your business. AI agents empower your teams to handle more pressing concerns that can have a ripple effect on business operations. 

It is time to leverage autonomous AI agents and streamline operations with built-in autonomy and decision-making capabilities. 

pagergpt is a powerful and scalable AI agent builder platform with pre-built AI agents, integrations, and industry-ready templates to help you hit the market quickly. You can also harness an orchestrator engine that enables you to build custom workflows for world-class use cases. 

The pagergpt advantage includes,

Visual workflow builder 

pagergpt offers a very intuitive drag-and-drop interface for creating your autonomous AI agents. 

Pre-built AI agents

You can start by selecting a pre-built AI agent and customizing it to meet your specific use case requirements. 

Built-in autonomy

pagergpt’s specialized sub-agents foster a modular and hierarchical structure that effectively enhances autonomy and enables diverse task handling. 

Fallback support

With pagergpt, you continue to provide an elevated CX with autonomous AI agents that intelligently route the call to human agents without losing context during handoffs. 

A/B testing autonomous responses

Once you have built your AI agent, use the chat interface in the playground. Ask your AI agent any question on trained data and verify its accuracy. 

Real-time monitoring dashboards

Leverage the pagergpt AI agent platform with a real-time monitoring dashboard to capture key KPIs and improve autonomous agent performance. 

What are you waiting for? It doesn’t take a dime to try pagergpt for free. Start building your first autonomous AI agent. Schedule a demo today.