Understanding AI Agents and How AWS Powers Their Evolution

Overview

Artificial Intelligence is revolutionizing software by making systems think and behave more autonomously. Central to this revolution are AI agents, software that can perceive, make decisions, and act towards ends. They collect information from their world, learn from it, and modify their behavior based on this knowledge. Through AWS, building and growing such smart agents is easier than ever. This post will explore what AI agents are and their major components.

Artificial Intelligence (AI) revolutionizes how we develop and engage with software, making applications smarter, more responsive, and more autonomous. At the center of this revolution is the idea of AI agents, intelligent entities that can sense their surroundings, make decisions, and act to accomplish certain objectives. As organizations look to harness the full potential of AI, cloud platforms like AWS provide powerful tools and services to design, deploy, and scale these agents across a wide range of use cases. In this blog, we will explore what AI agents are, how they operate, and how AWS enables their development and integration into real-world applications.

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Evolution of Agentic Infrastructure

The development of Large Language Model (LLM) interactions has come a long way, from initially simple prompt-response configurations to sophisticated systems such as Retrieval-Augmented Generation (RAG), controlled knowledge bases, and adaptive AI tools. These developments have led to influential concepts such as Multi-Agent Orchestration, where agents with specialized skills work together to accomplish intricate tasks, and Inline Agents, which facilitate real-time adjustment of AI assistants. As more capable AI systems are developed, they must address the critical design trade-off: supply sufficient information to ensure accurate response while preventing token overload, expense, and hallucinations. Contemporary agent architecture, single or multi-agent (MA), seeks to balance this requirement while maintaining the overall system as manageable as possible. In MA systems, agents cooperate within ordered hierarchies, e.g., Orchestrator, Supervisor, and Worker Agents, utilizing tools and knowledge bases, though such additional complexity introduces new issues of observability and assessment.

AI Agents

An AI agent is a system of technology that can make decisions, take actions, and learn with little or no human input, hence semi or completely autonomous. It incorporates machine learning or AI models and traditional software pieces. These agents typically have specific duties, run processes, or sift through data to help make decisions according to business needs. These agents are most developed to run around an end goal or goals to instruct them on their behavior and the outcomes.

An Amazon Bedrock Agent consists of a few core elements that work together to provide intelligent interactions and execute complex, multi-step tasks. The major parts of an agent are:

Foundation Model.
Prompts.
Action Group/Tools.
Memory.

Let’s consider each part separately:

Foundation Model:

The agent works on a selected foundation model as its core reasoning engine. It performs key functionalities like:

User input understanding.
Breaking down tasks into steps of logic.
Generating responses and selecting follow-up actions.

Although the foundation model has been described as the core of any generative AI system, it is only part of a larger architecture.

Prompts and Planning:

Programming defines the agent’s behavior and purpose by setting instructions as prompts for the foundation model. The prompts indicate how the agent works and how it interacts. There are two primary forms of prompts: system prompts and user prompts. System prompts define the agent’s general behavior and are typically specific, including response formatting rules, citation, and even personality or tone. Although these are generally invisible to end users, they can be tailored when installing a client agent. User prompts, on the other hand, are more explicit and task-oriented, ranging from straightforward questions to sophisticated requests for analysis or content creation.

Customizable prompt templates give developers the ability to fine-tune the agent’s behaviour at various stages of its workflow, including:

Pre-processing.
Orchestration.
Knowledge base response generation.
Post-processing.

These components enable Amazon Bedrock Agents to manage complex workflows, integrate with enterprise systems, and deliver intelligent, context-aware responses to user inputs.

Action Group/Tools:

In AI and agent-based systems, tools refer to software components that enable the AI to perform well-defined, deterministic tasks, such as executing scripts, interacting with external systems, or retrieving real-time data. These tools extend the capabilities of the AI model beyond natural language generation, allowing it to make API calls, query databases, and more. Each tool is defined using a JSON schema that outlines its functionality and required inputs, essentially serving as the Open API specification for a function the agent can call.

When building an Action Group, developers define the specific parameters the agent must collect from users to execute a given action. For instance, in a hotel booking scenario, an Action Group might include functions like CreateBooking, GetBooking, and CancelBooking, each requiring input such as check-in date, number of nights, or booking ID. The Bedrock agent uses this schema to guide conversations, asking users for the necessary information. Once all inputs are gathered, the corresponding AWS Lambda function is triggered, which executes the business logic, such as calling backend services or third-party APIs. This modular, schema-driven approach allows developers to build powerful, flexible AI assistants capable of performing various tasks through natural language interaction.

Memory

Agents are equipped with both short-term and long-term memory functions:

Short-term memory holds detailed information relevant to the ongoing conversation. Long-term memory retains key facts and summaries from past interactions for future reference.

Conclusion

AI agents are transforming how we build intelligent systems, enabling software to reason, act, and learn with minimal human involvement. With AWS services like Amazon Bedrock, developers have the infrastructure and tools to efficiently design, deploy, and scale these agents.

Agentic systems can handle increasingly complex tasks and deliver context-aware, real-time assistance by combining foundational models, dynamic prompts, memory, tools, and orchestration. In the subsequent blogs, we will see the details on the Action group and how we can leverage them.

Drop a query if you have any questions regarding AI agents and we will get back to you quickly.

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FAQs

1. What makes Amazon Bedrock suitable for building AI agents?

ANS: – Amazon Bedrock provides a managed platform that integrates foundation models, tools, memory, and orchestration, making it easier to build, scale, and maintain complex AI agents without needing to manage infrastructure.

2. What is the role of Action Groups in an AI agent?

ANS: – Action Groups define the specific tasks an agent can perform. They connect the agent’s language capabilities with real-world operations through APIs and Lambda functions.