The field of artificial intelligence has evolved dramatically, with AI agents emerging as one of the most transformative developments in recent years. This article synthesizes insights from leading research organizations, technology companies, and academic sources to provide a comprehensive understanding of AI agents, their architecture, capabilities, and real-world applications.

What Are AI Agents?

According to AWS:

An artificial intelligence (AI) agent is a software program that can interact with its environment, collect data, and use that data to perform self-directed tasks that meet predetermined goals. Humans set goals, but an AI agent independently chooses the best actions it needs to perform to achieve those goals. For example, consider a contact center AI agent that wants to resolve customer queries. The agent will automatically ask the customer different questions, look up information in internal documents, and respond with a solution. Based on the customer responses, it determines if it can resolve the query itself or pass it on to a human.

Multiple AI agents can collaborate to automate complex workflows and can also be used in agentic ai systems. They exchange data with each other, allowing the entire system to work together to achieve common goals. Individual AI agents can be specialized to perform specific subtasks with accuracy. An orchestrator agent coordinates the activities of different specialist agents to complete larger, more complex tasks.

From a more theoretical perspective, Wikipedia provides the following foundational definition:

In artificial intelligence, an intelligent agent is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge. AI textbooks define artificial intelligence as the “study and design of intelligent agents,” emphasizing that goal-directed behavior is central to intelligence.

A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods.

Intelligent agents can range from simple to highly complex. A basic thermostat or control system is considered an intelligent agent, as is a human being, or any other system that meets the same criteria—such as a firm, a state, or a biome.

Distinguishing Workflows from Agents

Anthropic, a leading AI research company, makes an important architectural distinction in their research on building effective agents:

“Agent” can be defined in several ways. Some customers define agents as fully autonomous systems that operate independently over extended periods, using various tools to accomplish complex tasks. Others use the term to describe more prescriptive implementations that follow predefined workflows. At Anthropic, we categorize all these variations as agentic systems, but draw an important architectural distinction between workflows and agents:

Workflows are systems where LLMs and tools are orchestrated through predefined code paths.

Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.

Key Principles That Define AI Agents

AWS identifies several key principles that distinguish AI agents from traditional software:

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.

The Performance Impact of Agentic Workflows

Andrew Ng, renowned AI researcher and founder of DeepLearning.AI, emphasizes the transformative potential of agentic workflows:

I think AI agent workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models. This is an important trend, and I urge everyone who works in AI to pay attention to it.

Today, we mostly use LLMs in zero-shot mode, prompting a model to generate final output token by token without revising its work. This is akin to asking someone to compose an essay from start to finish, typing straight through with no backspacing allowed, and expecting a high-quality result. Despite the difficulty, LLMs do amazingly well at this task!

With an agent workflow, however, we can ask the LLM to iterate over a document many times. For example, it might take a sequence of steps such as:

Plan an outline.

Decide what, if any, web searches are needed to gather more information.

Write a first draft.

Read over the first draft to spot unjustified arguments or extraneous information.

Revise the draft taking into account any weaknesses spotted.

And so on.

This iterative process is critical for most human writers to write good text. With AI, such an iterative workflow yields much better results than writing in a single pass.

Ng provides compelling data on the performance improvements achieved through agentic workflows:

GPT-3.5 (zero shot) was 48.1% correct. GPT-4 (zero shot) does better at 67.0%. However, the improvement from GPT-3.5 to GPT-4 is dwarfed by incorporating an iterative agent workflow. Indeed, wrapped in an agent loop, GPT-3.5 achieves up to 95.1%.

Agentic Design Patterns

DeepLearning.AI identifies four fundamental design patterns for building effective AI agents:

Reflection: The LLM examines its own work to come up with ways to improve it.

Tool Use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data.

Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal (for example, writing an outline for an essay, then doing online research, then writing a draft, and so on).

Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would.

Architecture of AI Agents

Key Components

AWS outlines the essential architectural components of AI agents:

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.

Building Block: The Augmented LLM

Anthropic describes the foundational building block of agentic systems:

The basic building block of agentic systems is an LLM enhanced with augmentations such as retrieval, tools, and memory. Our current models can actively use these capabilities—generating their own search queries, selecting appropriate tools, and determining what information to retain.

We recommend focusing on two key aspects of the implementation: tailoring these capabilities to your specific use case and ensuring they provide an easy, well-documented interface for your LLM. While there are many ways to implement these augmentations, one approach is through our recently released Model Context Protocol, which allows developers to integrate with a growing ecosystem of third-party tools with a simple client implementation.

Workflow Patterns for Agentic Systems

Anthropic identifies several proven workflow patterns that have been successful in production environments:

Workflow: Prompt chaining

Prompt chaining decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one. You can add programmatic checks (see “gate” in the diagram below) on any intermediate steps to ensure that the process is still on track.

When to use this workflow: This workflow is ideal for situations where the task can be easily and cleanly decomposed into fixed subtasks. The main goal is to trade off latency for higher accuracy, by making each LLM call an easier task.

Examples where prompt chaining is useful:

Generating Marketing copy, then translating it into a different language.

Writing an outline of a document, checking that the outline meets certain criteria, then writing the document based on the outline.

Workflow: Routing

Routing classifies an input and directs it to a specialized followup task. This workflow allows for separation of concerns, and building more specialized prompts. Without this workflow, optimizing for one kind of input can hurt performance on other inputs.

When to use this workflow: Routing works well for complex tasks where there are distinct categories that are better handled separately, and where classification can be handled accurately, either by an LLM or a more traditional classification model/algorithm.

Examples where routing is useful:

Directing different types of customer service queries (general questions, refund requests, technical support) into different downstream processes, prompts, and tools.

Routing easy/common questions to smaller, cost-efficient models like Claude Haiku 4.5 and hard/unusual questions to more capable models like Claude Sonnet 4.5 to optimize for best performance.

Workflow: Parallelization

LLMs can sometimes work simultaneously on a task and have their outputs aggregated programmatically. This workflow, parallelization, manifests in two key variations:

Sectioning: Breaking a task into independent subtasks run in parallel.

Voting: Running the same task multiple times to get diverse outputs.

When to use this workflow: Parallelization is effective when the divided subtasks can be parallelized for speed, or when multiple perspectives or attempts are needed for higher confidence results. For complex tasks with multiple considerations, LLMs generally perform better when each consideration is handled by a separate LLM call, allowing focused attention on each specific aspect.

Examples where parallelization is useful:

Sectioning:

Implementing guardrails where one model instance processes user queries while another screens them for inappropriate content or requests. This tends to perform better than having the same LLM call handle both guardrails and the core response.

Automating evals for evaluating LLM performance, where each LLM call evaluates a different aspect of the model’s performance on a given prompt.

Voting:

Reviewing a piece of code for vulnerabilities, where several different prompts review and flag the code if they find a problem.

Evaluating whether a given piece of content is inappropriate, with multiple prompts evaluating different aspects or requiring different vote thresholds to balance false positives and negatives.

Workflow: Orchestrator-workers

In the orchestrator-workers workflow, a central LLM dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results.

When to use this workflow: This workflow is well-suited for complex tasks where you can’t predict the subtasks needed (in coding, for example, the number of files that need to be changed and the nature of the change in each file likely depend on the task). Whereas it’s topographically similar, the key difference from parallelization is its flexibility—subtasks aren’t pre-defined, but determined by the orchestrator based on the specific input.

Example where orchestrator-workers is useful:

Coding products that make complex changes to multiple files each time.

Search tasks that involve gathering and analyzing information from multiple sources for possible relevant information.

Workflow: Evaluator-optimizer

In the evaluator-optimizer workflow, one LLM call generates a response while another provides evaluation and feedback in a loop.

When to use this workflow: This workflow is particularly effective when we have clear evaluation criteria, and when iterative refinement provides measurable value. The two signs of good fit are, first, that LLM responses can be demonstrably improved when a human articulates their feedback; and second, that the LLM can provide such feedback. This is analogous to the iterative writing process a human writer might go through when producing a polished document.

Examples where evaluator-optimizer is useful:

Literary translation where there are nuances that the translator LLM might not capture initially, but where an evaluator LLM can provide useful critiques.

Complex search tasks that require multiple rounds of searching and analysis to gather comprehensive information, where the evaluator decides whether further searches are warranted.

When to Use Agents vs. Simpler Solutions

Anthropic provides guidance on when to increase system complexity:

When building applications with LLMs, we recommend finding the simplest solution possible, and only increasing complexity when needed. This might mean not building agentic systems at all. Agentic systems often trade latency and cost for better task performance, and you should consider when this tradeoff makes sense.

When more complexity is warranted, workflows offer predictability and consistency for well-defined tasks, whereas agents are the better option when flexibility and model-driven decision-making are needed at scale. For many applications, however, optimizing single LLM calls with retrieval and in-context examples is usually enough.

Frameworks and Implementation Approaches

Anthropic discusses the role of frameworks in agent development:

There are many frameworks that make agentic systems easier to implement, including:

The Claude Agent SDK;

Strands Agents SDK by AWS;

Rivet, a drag and drop GUI LLM workflow builder; and

Vellum, another GUI tool for building and testing complex workflows.

These frameworks make it easy to get started by simplifying standard low-level tasks like calling LLMs, defining and parsing tools, and chaining calls together. However, they often create extra layers of abstraction that can obscure the underlying prompts ​​and responses, making them harder to debug. They can also make it tempting to add complexity when a simpler setup would suffice.

We suggest that developers start by using LLM APIs directly: many patterns can be implemented in a few lines of code. If you do use a framework, ensure you understand the underlying code. Incorrect assumptions about what’s under the hood are a common source of customer error.

Theoretical Foundation: Objective Functions

Wikipedia provides important context on the theoretical underpinnings of intelligent agents:

An objective function (or goal function) specifies the goals of an intelligent agent. An agent is deemed more intelligent if it consistently selects actions that yield outcomes better aligned with its objective function. In effect, the objective function serves as a measure of success.

The objective function may be:

Simple: For example, in a game of Go, the objective function might assign a value of 1 for a win and 0 for a loss.

Complex: It might require the agent to evaluate and learn from past actions, adapting its behavior based on patterns that have proven effective.

The objective function encapsulates all of the goals the agent is designed to achieve. For rational agents, it also incorporates the trade-offs between potentially conflicting goals. For instance, a self-driving car’s objective function might balance factors such as safety, speed, and passenger comfort.

Different terms are used to describe this concept, depending on the context. These include:

Utility function: Often used in economics and decision theory, representing the desirability of a state.

Objective function: A general term used in optimization.

Loss function: Typically used in machine learning, where the goal is to minimize the loss (error).

Reward Function: Used in reinforcement learning.

Fitness Function: Used in evolutionary systems.

Benefits of Using AI Agents

AWS highlights the key benefits organizations can realize from implementing AI agents:

Improved productivity

Business teams are more productive when they delegate repetitive tasks to AI agents. This way, they can divert their attention to mission-critical or creative activities, adding more value to their organization.

Reduced costs

Businesses can utilize intelligent agents to minimize unnecessary costs resulting from process inefficiencies, human errors, and manual processes. They can confidently tackle complex tasks because autonomous agents follow a consistent model that adapts to changing environments. Agent technology automating business processes can lead to significant cost savings.

Informed decision-making

Advanced intelligent agents have predictive capabilities and can collect and process massive amounts of real-time data. This enables business managers to make more informed predictions at speed when strategizing their next move. For example, you can use AI agents to analyze product demands in different market segments when running an ad campaign.

Improved customer experience

Customers seek engaging and personalized experiences when interacting with businesses. Integrating AI agents allows businesses to personalize product recommendations, provide prompt responses, and innovate to improve customer engagement, conversion, and loyalty. AI agents can provide detailed responses to complex customer questions and resolve challenges more efficiently.

Conclusion

AI agents represent a significant evolution in artificial intelligence, moving beyond simple query-response systems to autonomous entities capable of planning, reasoning, and adapting to achieve complex goals. As highlighted by leading researchers and organizations, the key to successful agent implementation lies in choosing the right level of complexity for the task at hand, understanding the underlying architecture, and leveraging proven design patterns.

The dramatic performance improvements demonstrated by agentic workflows—with GPT-3.5 achieving 95.1% accuracy when wrapped in an agent loop compared to just 48.1% in zero-shot mode—underscore the transformative potential of this approach. As the field continues to evolve, AI agents are poised to drive significant advances in automation, decision-making, and human-AI collaboration across industries.

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Sources

1. Anthropic Research – Building Effective Agents

https://www.anthropic.com/research/building-effective-agents

2. DeepLearning.AI – How Agents Can Improve LLM Performance

https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/

3. Amazon Web Services (AWS) – What Are AI Agents?

https://aws.amazon.com/what-is/ai-agents/

4. Wikipedia – Intelligent Agent

https://en.wikipedia.org/wiki/Intelligent_agent