Autonomous AI Agents Explained: What They Are and Why They Matter

We’re entering a new era of intelligent business operations with the emergence of autonomous agents. These systems do so much more than just automate tasks; they can act to pursue goals, learn from data, and adapt to their environments, all with with minimal oversight from people. Autonomous agents are becoming more and more important for companies that want to make faster decisions, lower costs, and become more agile.

In fact, the autonomous AI agents market was valued at near $6.8 billion in 2024 and is expected to grow at more than 30 percent annually through 2034. Why? Because businesses are looking for smart, scalable, and self-guided systems that don’t just follow orders, but take action.

So what does this mean for you and your organization? With autonomous agents, you can have a fleet of goal-driven agents, each one handling specific tasks in different parts of your business. They can draw from your trusted, governed data in real timeso every action is informed, compliant, and accurate.

In this article, we’ll discuss what autonomous agents are, how they work, and what benefits they bring. We’ll cover using them responsibly and why investing in autonomous AI is a smart decision. Plus, we’ll highlight how Domo’s secure, governed platform can help your business reach its full potential.

The spectrum of AI: Understanding the archetypes

A great place to start is to step back and look at where autonomous AI sits on the AI maturity spectrum and why it’s the logical next step for businesses ready to scale intelligent automation.

AI isn’t a one-size-fits-all solution; it comes in many forms, each with its own strengths and qualities. Understanding how and where autonomous AI agents fit in can help you find the right tool for the right job. So let’s take a closer look at the different types of AI product on the AI spectrum.

1. Assistive AI

Assistive AI is about augmenting the way people work. These systems use smart technologies to help individuals by automating repetitive tasks and making it easier to make informed decisions.  

For example, tools like Grammarly suggest writing improvements or help draft emails. An AI-powered research assistant like Perplexity AI can speed up data analysis and summarize documents quickly. These tools rely heavily on human input and supervision, which makes them ideal for supporting specific tasks.

2. Agentic AI

Agentic AI is designed to carry out simple, multi-step tasks, while working autonomously. It uses an LLM (or similar model) to reason, plan, and initiate actions within specific, defined workflows.  

For example, an AI agent might extract invoice data, validate it, and send it for approval, all with minimal oversight. These agents still rely on human-set parameters and periodic monitoring while they act intelligently.  

3. Orchestrative AI  

Orchestrative AI (also called AI orchestration) is about getting different AI tools and systems to work together. It helps manage the flow of data between tool, making sure tasks are assigned to the right agents and resources are usedefficiently. And when issues do come up, it knows how to respond and keep things running smoothly.

For example, in e-commerce, orchestrative AI might bring together a recommendation engine and a pricing tool. This helps show customers the right products at the right prices in real time. At the same time, an inventory agent works behind the scenes to make sure popular items stay in stock.

4. Autonomous AI

With autonomous AI, systems are able to operate on their own, making decisions and carrying out tasks within limits set by people. What sets these systems apart is their ability to reason through complex scenarios, adapt to new information, and take proactive action when needed.

For instance, Tesla’s Full Self-Driving (FSD) system uses autonomous AI to navigate roads, make real-time decisions, and adapt to traffic conditions. It perceives its environment, reasons through driving strategies, and acts while learning from each trip.

Now that we have mapped the AI spectrum, let’s define what makes an autonomous AI agent unique and discuss their characteristics.

What exactly is an autonomous AI agent?

An autonomous AI agent is more than a smart tool. It’s an advanced AI system that acts on its own with little to no human intervention. Give it an objective, and it figures out how to get there by creating and executing a sequence of tasks by itself. It will then keep working until it achieves the overall goal.

Now, what makes an autonomous AI agent different from a regular AI agent or a simpler AI?  

An AI agent can perceive its environment and take action to achieve a specific goal. It could be something simple, like a chatbot that answers questions using pre-programmed logic, or a recommendation engine that suggests movies based on a user's past viewing. These agents usually follow a set plan and need frequent guidance or prompts from a user or another system. They're reactive (able to respond to inputs), but they don’t plan multiple steps ahead or operate independently for long.

However, only an autonomous AI agent can plan and make its own decisions across multiple steps. It can adapt, set subgoals, and continue working toward a high-level objective after receiving just one instruction.

To understand their capabilities, let’s discuss the core traits that define autonomous AI agents.

Key characteristics of autonomous agents

• Independent goal-setting and execution: Agents interpret business objectives and translate them into actionable plans. They maintain internal goals and plans, using reasoning engines to evaluate options and select the best course of action. Unlike prompt-based systems, they act independently without step-by-step guidance.

• Multi-step task completion: Autonomous agents complete several tasks in a row, like ordering supplies, generating reports, and notifying teams, by chaining together API calls, tool interactions, and decisions. The agent decides whether to execute tasks sequentially or in parallel depending on task requirements.

• Sequential execution: This ensures order and data consistency, for example, updating inventory, then sending notifications.

• Parallel execution: This speeds up independent tasks, such as querying multiple databases simultaneously and aggregating results.

• Adaptive learning over time: Autonomous systems improve themselves over time through continuous learning mechanisms. They refine their decision-making strategies, interactions with both the digital environment and humans, and their ability to achieve results, often using reinforcement learning.

• Integration with external tools and data: Autonomous agents use tools such as APIs, RAG systems, databases, and CRMs to fetch data and trigger processes. They ensure their actions are grounded in trusted, up-to-date data.  

• Memory: Effective autonomy depends on memory systems that support both immediate context and long-term learning. Short-term memory tracks ongoing tasks, while long-term memory stores patterns, preferences, and decisions. This helps autonomous agents remember experiences, stay consistent, and customize their actions over time.

Now that we have defined what an autonomous AI agent is, let’s explore why investing in them delivers concrete business value.

Why invest in autonomous AI?

Autonomous AI agents are here to help businesses work better and save money. These self-directed assistants offer benefits that empower organizations to stay competitive and agile by automating tasks and making decisions on their own.  

Over the next year, more than 60 percent of organizations plan to create human-agent teams, where these AI agents serve as subordinates or support human skills.

Here are the key benefits of adopting autonomous systems:

• 24/7 operational efficiency: Autonomous AI agents operate around the clock, providing continuous monitoring and action without the limitations of human schedules. These agents can manage multiple tasks simultaneously, ensuring that no opportunity or threat goes unnoticed.

• Strategic focus for human teams: Autonomous agents free employees to focus on strategic and high-value work by offloading repetitive, high-volume tasks. According to McKinsey, AI task automation could boost global productivity growth by 0.8 to 1.4 percent each year.  

• Scalable decision-making: Equipped with real-time analytics and pattern-detection, autonomous agents identify trends and anomalies that human analysts might miss.  

• Reduced human error: Repetitive human tasks are prone to errors from fatigue or oversight. Autonomous agents follow consistent logic and validate inputs, minimizing mistakes. Pharmacy agents' cross-referencing drug interactions helps prevent over 200,000 medication errors annually in the US healthcare system.

• Rapid response: Autonomous agents respond immediately to real-time triggers, removing delays in critical workflows. For example, in manufacturing, predictive maintenance agents can spot issues early and schedule repairs before anything breaks down. In cybersecurity, they act quickly to isolate threats, stopping breaches faster than a human team could react.

Now that we have explored their value, let’s dive into how these autonomous AI agents operate to deliver such transformative outcomes.

How autonomous agents operate

Knowing how AI agents work helps businesses make better choices about using them. Essentially, autonomous AI agents rely on ongoing feedback from their environment to help manage their own processes effectively.  

Below is the working process of autonomous agents.

Perceive: Gather and interpret information

An autonomous agent begins with perception, where it collects and interprets data from its environment. This may include structured and unstructured data. This data can come from various sources, such as:  

• Sensors: Sensors that detect temperature, pressure, light, sound, and motion supply real-time information about the agent's surroundings.

• Databases: AI agents can access and retrieve data from databases, knowledge graphs, or other data sources.

• User inputs: Humans can give input to AI agents using different interfaces, like voice commands, text inputs, or gestures.

• IoT devices: AI agents can gather data from Internet of Things (IoT) devices, such as smart home gadgets, wearables, or industrial sensors.  

The perception component processes this data using various techniques to identify relevant data points, including:

• Data filtering: Removing noise, outliers, or irrelevant data to improve data quality.

• Data transformation: Converting data into a proper format for processing and analysis.

• Feature extraction: Identifying important features or patterns in the data.

Reason: Decide actions using LLM-based planning

Next, the agent enters the reasoning phase, where it interprets processed data to generate actionable plans. This often uses a large language model (LLM) or a combination of planning algorithms and domain knowledge. In this phase, the agent:

• Analyzes context: Understands current conditions, constraints, and objectives.

• Plans actions: Breaks down high-level goals into structured sub-tasks or workflows.

Act: Perform tasks via tools and APIs

With a plan in place, the autonomous AI agent takes action by interacting with external systems, tools, or APIs to execute tasks. This step transforms decisions into outcomes, whether it’s answering a question, recommending a product, updating databases, or triggering workflows.  

Learn: Refine behavior from outcomes

After taking action, the agent assesses outcomes against defined performance criteria, such as task completion rates, execution speed, data accuracy, or user satisfaction. These benchmarks are typically set during agent configuration. They can include both quantitative metrics and qualitative signals.  

Based on this evaluation, the agent applies feedback mechanisms like:

• Reinforcement learning algorithms, where positive or negative rewards guide future policy changes

• Heuristic updates, where thresholds are adjusted or logic is rerouted to improve accuracy

• Self-assessment loops, where the agent identifies errors, hypothesizes fixes, and tests them automatically

This ongoing feedback loop enhances AI automation by refining the agent’s future actions.

Now that we understand how autonomous AI agents work, let’s explore their real-world applications.

Real-world applications and use cases

Autonomous AI agents are valuable in different fields because they can work on their own to complete a wide range of tasks with little input from people. Here are some key examples from various industries where they are making an impact.

Supply chain  

Autonomous AI agents can handle much of supply chain management on their own, without needing human input. They autonomously monitor inventory, forecast demand, and manage logistics. Autonomous AI agents can also analyze live data from suppliers, warehouses, and market trends to make their decisions, reducing costs and preventing disruptions.  

• Example: Maersk, a top global shipping company, uses autonomous AI agents in its logistics. These agents track data from thousands of ships, trucks, and containers to monitor movements, predict delays, and adjust routes.  

Financial services  

In the financial services industry, banks can use autonomous agents to handle transaction disputes across various channels like banking apps, SMS, websites, or phone calls. They can also detect fraud, manage risk, and even execute trades.

In insurance, agents can automatically adjust coverage options, offer better prices, and provide coverage to eligible policyholders. They can also update beneficiaries, send claims adjusters, and even issue claims checks or renew policies.

• Example: PayPal, a leading online payment platform, uses AI agents to monitor transaction patterns in real time to detect fraudulent activities. They reported a 30 percent reduction in fraud rates by using an autonomous AI system.

Cybersecurity  

Cybersecurity requires fast responses to any emerging threats. Autonomous AI agents help by automatically detecting, analyzing, and neutralizing threats faster than manual processes could. These agents monitor network activity and execute countermeasures, ensuring strong real-time protection.  

• Example: Trend Micro’s AI Brain, an autonomous cybersecurity agent that evaluates threat data and autonomously applies patches or containment actions, freeing up security teams from manual triage.

Customer service  

Autonomous AI agents can help in customer support by providing users with instant personalized answers to their queries, resolving their common issues, and guiding them through processes. Enterprises can also use autonomous agents to analyze customer data and identify potential issues.  

• Example: An agent observing server performance might identify an anomaly that could cause service disruption for a customer. Then, it could automatically initiate a fix or proactively notify the technical team.

Manufacturing  

For manufacturing, autonomous agents optimize production by monitoring and predicting equipment failure and scheduling preventative actions before failures occur.

• Example: Siemens uses AI agents to monitor real-time data from its machines, helping them spot issues before they occur. After deploying autonomous AI, they cut unplanned downtime by 25 percent.

Let’s examine how Domo helps organizations to build AI agent solutions with trust and security.

The Domo advantage: Building governed, secure, and effective AI

If you’re curious what we at Domo think about autonomous AI, our philosophy is clear: AI must be secure, governed, and effective by design.

That’s why Domo’s in-platform AI, including DomoGPT, operates solely with your data, which is never used to train models. Your data remains encrypted and securely stored within our private cloud, which complies with stringent SOC 2, ISO, and HIPAA security standards.

But security is just the start. We layer governance controls across every autonomous operation: standardizing data, defining ownership, tracking lineage, and auditing every action in real time. It’s about creating a transparent and compliant environment where AI agents can act confidently. And so can you.

Built on this secure foundation, Agent Catalyst empowers you to build autonomous agents that manage end-to-end workflows, analyze data, execute actions, and learn over time. All while upholding full transparency and giving you the ability to step in when needed.

Want to dive deeper into how AI agents are changing the enterprise today? Watch the replay of our Agentic AI Summit.