Autonomous Agent

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What is an Autonomous Agent?

An autonomous agent is an artificial intelligence (AI) program that can perform complex tasks with a degree of independence. This type of software agent uses chain of thought logic to break complex tasks down into a series of subtasks that can be executed programmatically with minimal human help.


Automated agents can use large language models (LLMs) to interpret different types of input data and generate resources to execute subtasks.

The information technology (IT) research firm Gartner predicts that by 2028, one-third of all the interactions with generative AI platforms like ChatGPT and Google Gemini will be initiated by autonomous agents.

Humans will be responsible for setting high-level objectives, and autonomous agents will be responsible for figuring out what’s required to achieve the objective and following through.

Techopedia Explains the Autonomous Agent Meaning

Techopedia Explains the Autonomous Agent Meaning

In the context of an autonomous agent definition, the term agent refers to software that performs tasks on behalf of users or other systems. Autonomous agents are a specific type of software agent that are characterized by their high degree of independence and self-directed behavior.

Autonomous agents can be bots that operate strictly within digital environments or programs that physical robots use to interact with the real world. API calls allow this type of software agent to retrieve information, interact with other software applications, and control hardware components.

How Autonomous Agent Works

Autonomous agents are designed to accomplish a specific objective. Like other types of software agents, autonomous agents typically operate within the scope defined by their initial programming and the permissions that are granted to them. For example, if they can make API calls to collect data, their ability to make calls is usually constrained to a predefined set of APIs or services.

Here is a high-level view of the five steps an autonomous agent might take to achieve a defined objective:

  1. The agent will break a complex task into subtasks and use intake mechanisms to gather the data it needs to complete each subtask. Depending on the objective, the data may be acquired from cameras, proximity sensors, microphones, GPS systems, accelerometers, API calls, or other types of digital inputs. The quality and type of data that an agent can gather can significantly impact the agent’s understanding of its surroundings and the context of a given objective.

  2. The autonomous agent then processes the data it has collected. This step might initially require filtering out noisy data or extracting relevant features from large amounts of data.
  3. Next, the autonomous agent analyzes the processed data so it can make data-driven decisions when completing subtasks. For simple autonomous agents, the decision-making process might be based on predefined rules or algorithms that map specific inputs to specific outputs. More sophisticated agents typically use deep learning algorithms that can weigh different options, consider potential outcomes, and make decisions that are closely aligned with the defined objective.

  4. Once a decision has been made, the agent can initiate an action through its output mechanisms to execute the subtask. In manufacturing robot systems, this might involve actuators that enable movement. In software systems, this might involve engineering prompts to create subtask resources, modify a digital environment, send a command to another system, or adjust an internal operational parameter.

  5. The steps above are repeated until all subtasks have been executed and the defined objective has been reached.

Types of Autonomous Agents

Autonomous agents can be classified by their operational capabilities, their levels of autonomy, and the complex tasks they are able to perform.

Reactive Autonomous Agents
Make decisions based on the current state of their environment. When they receive new data, they will immediately react to it without retaining any memory of past states. 

Deliberative Autonomous Agents
Known as cognitive or reasoning agents, can make decisions based on their analysis of the environment and the user’s objectives. They can model the world, consider various possible actions, and choose the one that best achieves their goals.

Hybrid Autonomous Agents
Combine elements of both reactive and deliberative agents. They can react to immediate changes in their environment while also pursuing long-term goals.
Model-Based Autonomous Agents
Can be programmed or use machine learning algorithms to understand their environment. They can deal with incomplete data by filling in knowledge gaps with predictions based on past observations.
Goal-Based Autonomous Agents
Make decisions by evaluating how likely a specific action will help achieve a specific objective. This type of agent can make internal adjustments based on changes in the environment or objective. 
Utility-Based Autonomous Agents
Evaluate actions based on a utility function that ranks outcomes according to their desirability. They can optimize their performance in real or near-real time according to predefined criteria.

Why Do We Need Autonomous Agents?

Autonomous agents are sometimes viewed as the next version of digital transformation. They have the potential to revolutionize various industries by improving workflow management and streamlining business processes.

Autonomous agents can operate continuously without the need for breaks or sleep. They are ideal for completing tasks that are time-consuming or demand high levels of precision and consistency.

They can also be used to minimize human exposure to risk, help meet production goals in a cost-effective manner, and enhance both backend and user-facing processes in business, finance, agriculture, manufacturing, healthcare, and other niche domains.

Autonomous Agent Examples and Use Cases

The following examples and use cases illustrate the wide-ranging applications of autonomous agents across various domains:

Customer Service ChatbotsWarehouse RobotsEducational AgentsFintech AgentsSelf-Driving Cars

Can independently resolve common issues, escalate complex queries, and learn to provide more personalized assistance over time.

Can optimize the way orders are picked and packed in real time.

Can use data from a student’s past evaluations to create data-driven learning paths that are aligned with the student’s strengths and weaknesses.

Can analyze market data, identify trends, and execute trades with minimal human intervention.

Can use proximity sensors, computer vision, GPS, and complex AI algorithms to navigate roads, recognize traffic signals, and avoid obstacles.

Autonomous Agent Pros and Cons

The advantages and disadvantages of autonomous agents depend on the type of agent used to meet a specific objective, the data the agent uses to make decisions, and the object the agent is tasked with completing.

In general, each autonomous agent meaning provides advantages for use cases that involve:

  • Planning and executing complex tasks with minimal human assistance.
  • Enhancing human productivity and efficiency.
  • Performing repetitive tasks with high precision and minimal error.
  • Working in dangerous environments that are hazardous for humans.
  • Reducing the cost of human labor.

On the flip side, autonomous agents are amplifying people’s concerns about artificial intelligence and the technology’s potential to replace humans in the workplace. The technology has inspired media coverage about the need for upskilling and educating students about AI because they will be tomorrow’s workforce.

Some critics of the technology have expressed concerns about the high cost of developing, implementing, and maintaining autonomous agents. Proponents of the technology have pointed out that autonomous agents already exist in several niche domains, and it won’t be long before the average person can purchase an autonomous agent to complete common multi-step tasks.

Future of Autonomous Agent

As autonomous agents become more common, it’s expected there will be an increased focus on addressing the ethical and regulatory challenges associated with their deployment.

Standardized frameworks, guidelines, and regulations to ensure the responsible development and use of autonomous agents will likely play an important role in shaping their future.

Gartner thinks that the increased availability of multimodal genAI platforms has contributed to the growing interest in autonomous agents. Because multimodal platforms can understand and generate more than one type of data, it is easier than ever for one type of machine learning model to collaborate with another type of machine learning model.

Companies are advised to prepare for the mainstream adoption of autonomous agents within three to five years by developing a transformation roadmap that identifies what types of complex tasks could potentially be completed by autonomous agents and how to integrate autonomous AI agents into current workflows.

The Bottom Line

Autonomous agents are software agents that can be given a high-level objective, break the objective down into manageable subtasks, collect and analyze data to make decisions and execute subtasks, and use feedback to improve the entire process.

As AI and machine learning technologies continue to evolve, autonomous agents will become more intelligent, adaptable, and capable of handling increasingly complex tasks. This will enable them to be used for a wider range of use cases.


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Margaret Rouse
Technology Expert
Margaret Rouse
Technology Expert

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.