What are AI Agents?

All software autonomously performs various routine tasks as specified by the software developer. So, what makes AI agents special?

Autonomy

AI agents act autonomously, without constant human intervention. While traditional software follows hard-coded instructions, AI agents identify the next appropriate action based on past data and execute it without continuous human oversight.

For example, a bookkeeping agent automatically flags and requests missing invoice data for purchases.

Goal-oriented behavior

AI agents are driven by objectives. Their actions aim to maximize success as defined by a utility function or performance metric. Unlike traditional programs that merely complete tasks, intelligent agents pursue goals and evaluate the consequences of their actions in relation to those goals.

For example, an AI logistics system optimizes delivery routes to balance speed, cost, and fuel consumption simultaneously, thereby balancing multiple objectives.

Perception

AI agents interact with their environment by collecting data through sensors or digital inputs. They can collect data from external systems and tools via APIS. This data allows them to perceive the world around them, recognize changes, and update their internal state accordingly.

For example, cybersecurity agents collect data from third-party databases to remain aware of the latest security incidents.

Rationality

AI agents are rational entities with reasoning capabilities. They combine data from their environment with domain knowledge and past context to make informed decisions, achieving optimal performance and results.

For example, a robotic agent collects sensor data, and a chatbot uses customer queries as input. The AI agent applies the data to make an informed decision. It analyzes the collected data to predict the best outcomes that support predetermined goals. The agent also uses the results to formulate the next action that it should take. For example, self-driving cars navigate around obstacles on the road based on data from multiple sensors.

Proactivity

AI agents can take initiative based on forecasts and models of future states. Instead of simply reacting to inputs, they anticipate events and prepare accordingly.

For instance, an AI-based customer service agent might reach out to a user whose behavior suggests frustration, offering help before a support ticket is filed. Autonomous warehouse robots may reposition themselves in anticipation of upcoming high-traffic operations.

Continuous learning

AI agents improve over time by learning from past interactions. They identify patterns, feedback, and outcomes to refine their behavior and decision-making. This differentiates them from static programs that always behave the same way regardless of new inputs.

For instance, predictive maintenance agents learn from past equipment failures to better forecast future issues.

Adaptability

AI agents adjust their strategies in response to new circumstances. This flexibility allows them to handle uncertainty, novel situations, and incomplete information.

For example, a stock trading bot adapts its strategy during a market crash, while a game-playing agent like AlphaZero discovers new tactics through self-play, even without prior human strategies.

Collaboration

AI agents can work with other agents or human agents to achieve shared goals. They are capable of communicating, coordinating, and cooperating to perform tasks together. Their collaborative behavior often involves negotiation, sharing information, allocating tasks, and adapting to others' actions.

For example, multi-agent systems in healthcare can have agents specializing in specific tasks like diagnosis, preventive care, medicine scheduling, etc., for holistic patient care automation.

An AI agent architecture contains the following key components.

Foundation model

At the core of any AI agent lies a foundation or large language model (LLM) such as GPT or Claude. It enables the agent to interpret natural language inputs, generate human-like responses, and reason over complex instructions. The LLM acts as the agent's reasoning engine, processing prompts and transforming them into actions, decisions, or queries to other components (e.g., memory or tools). It retains some memory across sessions by default and can be coupled with external systems to simulate continuity and context awareness.

Planning module

The planning module enables the agent to break down goals into smaller, manageable steps and sequence them logically. This module employs symbolic reasoning, decision trees, or algorithmic strategies to determine the most effective approach for achieving a desired outcome. It can be implemented as a prompt-driven task decomposition or more formalized approaches, such as Hierarchical Task Networks (HTNs) or classical planning algorithms. Planning allows the agent to operate over longer time horizons, considering dependencies and contingencies between tasks.

Memory module

The memory module allows the agent to retain information across interactions, sessions, or tasks. This includes both short-term memory, such as chat history or recent sensor input, and long-term memory, including customer data, prior actions, or accumulated knowledge. Memory enhances the agent’s personalization, coherence, and context-awareness. When building AI agents, developers use vector databases or knowledge graphs to store and retrieve semantically meaningful content.

Tool integration

AI agents often extend their capabilities by connecting to external software, APIs, or devices. This allows them to act beyond natural language, performing real-world tasks such as retrieving data, sending emails, running code, querying databases, or controlling hardware. The agent identifies when a task requires a tool and then delegates the operation accordingly. Tool use is typically guided by the LLM through planning and parsing modules that format the tool call and interpret its output.

Learning and reflection

Reflection can occur in multiple forms:

• The agent evaluates the quality of its own output (e.g., did it solve the problem correctly?).
• Human users or automated systems provide corrections.
• The agent selects uncertain or informative examples to improve its learning.

Reinforcement Learning (RL) is a key learning paradigm. The agent interacts with an environment, receives feedback in the form of rewards or penalties, and learns a policy that maps states to actions for maximum cumulative reward. RL is especially useful in environments where explicit training data is sparse, such as robotics, gaming, or financial trading. The agent balances exploration (trying new actions) and exploitation (using known best actions) to improve its strategy over time.

Organizations create and deploy AI agents across a range of types and tasks. We share some examples below.

Simple reflex agents

A simple reflex agent operates strictly based on predefined rules and its immediate data. It will not respond to situations beyond a given event, condition, and action rule. Hence, these agents are suitable for simple tasks that don’t require extensive training. For example, you can use a simple reflex agent to reset passwords by detecting specific keywords in a user’s conversation.

Model-based reflex agents

A model-based agent is similar to simple reflex agents, except that it has a more advanced decision-making mechanism. Rather than merely following a specific rule, a model-based agent evaluates probable outcomes and consequences before making a decision. Using supporting data, it builds an internal model of the world it perceives and uses that to support its decisions.

Goal-based agents

Goal-based agents, also known as rule-based agents, are AI agents that possess more robust reasoning capabilities. Besides evaluating the environment data, the agent compares different approaches to help it achieve the desired outcome. Goal-based agents always choose the most efficient path. They are suitable for performing complex tasks, such as natural language processing (NLP) and robotics applications.

Utility-based agents

A utility-based agent employs a complex reasoning algorithm to assist users in maximizing the outcome they desire. The agent compares different scenarios and their respective utility values or benefits. Then, it selects one that offers users the most rewards. For example, customers can use a utility-based agent to search for flight tickets with the minimum travel time, regardless of the price.

Learning agents

A learning agent continually learns from past experiences to enhance its performance. Using sensory input and feedback mechanisms, the agent adapts its learning element over time to meet specific standards. Additionally, it utilizes a problem generator to design new tasks that train itself using collected data and past results.

Hierarchical agents

Hierarchical agents are an organized group of intelligent agents arranged in tiers. Higher-level agents decompose complex tasks into smaller ones and assign them to lower-level agents. Each agent runs independently and submits a progress report to its supervising agent. The higher-level agent collects the results and coordinates subordinate agents to ensure they collectively achieve goals.

Multi-agent systems

A multi-agent system (MAS) consists of multiple agents that interact with one another to solve problems or achieve shared objectives. These agents can be homogeneous (similar in design) or heterogeneous (different in structure or function) and may collaborate, coordinate, or even compete depending on the context. MAS are particularly effective in complex, distributed environments where centralized control is impractical.

For example, in autonomous vehicle fleets, each vehicle acts as an independent agent but collaborates with others to avoid traffic congestion and prevent collisions, leading to smoother traffic flow.

Amazon Bedrock is a fully managed service that provides easy access to industry-leading generative AI models, such as Claude, Llama 2, and Amazon Titan, along with a broad set of capabilities needed to build generative AI applications.

Amazon Bedrock Agents use the reasoning of FMs, APIs, and data to break down user requests, gather relevant information, and efficiently complete tasks. Building an agent is straightforward and fast, with setup in just a few steps. Amazon Bedrock supports:

• Memory retention for seamless task continuity
• Multi-agent collaboration to build multiple specialized agents under the coordination of a supervisor agent
• Amazon Bedrock Guardrails for built-in security and reliability.

AWS has introduced an open-source toolkit with a growing catalog of starter agents purpose-built for healthcare and life sciences use cases.

AWS Transform is the first agentic AI service for transforming .NET, mainframe, and VMware workloads. Built on 19 years of migration experience, it deploys specialized AI agents to automate complex tasks like assessments, code analysis, refactoring, decomposition, dependency mapping, validation, and transformation planning. It helps organizations to simultaneously modernize hundreds of applications while maintaining quality and control.

Amazon Q Business is a generative AI-powered assistant designed to help you find information, gain insights, and take action at work. It puts the power of AI agent creation in the hands of every employee. Anyone can use it to create lightweight agentic AI apps that interact with common enterprise software and automate repetitive tasks.

Get started with AI agents on AWS by creating a free account today.