Building an AI Agent involves creating a computer system that can make decisions, choose tools, and take actions to achieve specific goals autonomously.

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How to build an AI agent?
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In the wake of ChatGPT’s release by OpenAI on November 30, 2022, the subsequent
year saw a surge in AI chat applications, marking 2023 as the year of conversational AI.
Competitors emerged, and the development of chat apps using Retrieval Augmented
Generation (RAG) became increasingly popular. As we step into 2024, the focus is
shifting towards AI agents, which promise to extend the capabilities of AI beyond mere
question-answering. These agents integrate Large Language Models (LLMs) with specific
tools and memory, enabling them to perform a variety of tasks to enhance their
functionality and assist users in more sophisticated ways.
AI agents are gaining recognition in the AI trends matrix, with their potential for adoption
being increasingly acknowledged. They can operate autonomously to varying degrees,
from executing simple tasks like fetching information in a “web browser” to formulating
and executing multi-step plans for more complex objectives. Beyond traditional robotic
process automation (RPA), AI agents are becoming more adaptable and intelligent,
capable of supporting ongoing business processes.
One notable example is Thoughtworks‘ project with a Canadian telecoms company,
where AI agents were used to modernize fragmented systems, demonstrating the
potential for fully autonomous agents to solve problems in the background. Semi-
autonomous approaches are also viable, where a customer service representative can
instruct an AI agent to implement a solution.

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The ability of AI agents to interface with corporate systems and real-world data via APIs is
particularly intriguing. The integration of OpenAI’s GPT models with tools like Zapier,
which connects to over 6,000 corporate systems including Trello and Jira, exemplifies this
development. Other platforms like Amazon’s Bedrock and Google’s Duet AI are also
exploring the possibilities of AI agents in interfacing with various systems and data
sources.
As the landscape of AI continues to evolve, AI agents are poised to play a crucial role in
advancing the capabilities of AI in business and beyond.
The goal of this article is to provide a comprehensive understanding of AI agents and to
guide readers through the steps of how to build an AI agent system using AutoGen, a
platform that simplifies the orchestration, optimization, and automation of large language
model workflows.
Understanding AI agents
Working mechanism of an agent
The different functional architectural blocks of an autonomous AI agent
Building an AI agent – the basic concept
Microsoft Autogen- an overview
How to build AI agents with Autogen : Essential steps
Benefits of Autogen
How LeewayHertz can help you build AI agents
Understanding AI agents
An AI agent is a highly efficient, intelligent virtual assistant that autonomously performs
tasks by leveraging artificial intelligence. It is designed to sense its environment, interpret
data, make informed decisions, and execute actions to achieve predefined objectives.
In a corporate context, AI agents enhance efficiency by automating routine tasks and
analyzing complex data, thereby allowing employees to concentrate on strategic and
creative endeavors. These agents complement human efforts rather than replace them,
facilitating a more productive and effective workforce.
AI agents are characterized by their proactivity and decision-making capabilities. Unlike
passive tools, they actively engage in their environment, making choices and taking
actions to fulfill their designated goals.
A critical aspect of AI agents is their capacity for learning and adaptation. Through the
integration of technologies such as Large Language Models, they continuously improve
their performance based on interactions, evolving into more sophisticated and intelligent
assistants over time.
In case of Autonomous AI Agents, multiple agents collaborate, each assuming
specialized roles akin to a professional team. This collaborative approach allows for a
more comprehensive and efficient problem-solving process, as each agent contributes its

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expertise to achieve a common objective.
Let’s imagine a scenario with Jordan, a salesperson, and their custom AI assistant.
Jordan starts their day by checking their emails and finds a message from a potential
client, Sam, who’s interested in their company’s premium services. Jordan’s AI assistant,
which is connected to their email, has been keeping track of these interactions. Using
what it has learned from Jordan’s past replies and the company’s information, the AI
agent drafts a response. This includes a summary of the premium services, their
advantages, and a tailored suggestion for Sam based on his interests and needs.
Jordan looks over the draft in their email, adds their personal touch, and sends it off. The
AI agent then proposes follow-up steps, like setting up a call with Sam, sending a detailed
brochure, or reminding Jordan to follow up if there’s no reply in a week.
Jordan agrees to these steps, and the AI organizes their calendar, emails the brochure,
and sets reminders in their digital planner. With the AI handling these routine yet
important tasks, Jordan can concentrate on other critical aspects of their job.
Working mechanism of an agent
Building autonomous agents requires emulating human cognitive processes and
strategically planning task execution. In this phase, LLM agents have the ability to
decompose large and intricate tasks into smaller, more manageable segments.
Furthermore, these agents possess the capacity for self-reflection and learning from
previous actions and errors, thereby enhancing future performance and improving
outcomes.
Let’s begin by defining an agent as a software program that performs tasks on behalf of a
user. The ability of Large Language Models (LLMs) to emulate human-like cognitive
processes opens up new avenues for tasks that were previously challenging or
unfeasible.
At its most basic, an LLM-based agent is a program that encapsulates ChatGPT with a
text interface capable of executing tasks such as document summarization.
The concept of “agent orchestration” introduces a higher level of complexity. For instance,
two specialized agents could collaborate on your code—one focusing on code generation
and the other on code review. Alternatively, you could enhance an agent with a tool like
an API that provides access to internet search. Or you could improve an agent’s
intelligence and reliability by providing additional context through techniques like Retrieval
Augmented Generation (RAG).
The most advanced agents are termed “autonomous.” These are programs capable of
handling sequential tasks, iterating, or pursuing objectives with minimal or even no
human intervention. Consider fraud detection—an autonomous agent can adjust its
behavior to identify intricate and evolving patterns of fraud, significantly reducing false

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positives, ensuring legitimate transactions are not mistakenly flagged as fraudulent. It can
also detect and prevent fraud in real-time by determining the appropriate actions to take,
thereby saving both time and resources.
The graphic below illustrates a basic framework of an autonomous agent that processes
inputs from users or triggers from applications.
User Input
Observer
Agent
Event & Context
Analysis
Get & Update
Context
Leverage Tools
Complete
Tasks
Priorise & Clean up Taskqueue
Influence &
Notify
Autonomous Agent
Create Task with
Context
Task Queue
Memory/Context
Execution
Agent
Priorisation
Agent
Responses
Action(s)
Tools
Event(s)
LeewayHertz
The described autonomous agent is a sophisticated system comprising various
specialized agents collaborating seamlessly. An observer agent evaluates incoming
information, enriches it with pertinent context, and then either stores it in its memory or
adds it to the task queue. For instance, in a business process analyzing credit card
transaction events for fraud, a single use of a credit card may not be significant, but two
uses within a short time frame across different continents could indicate fraud.
The initial event might lead the agent to simply store the information in memory. However,
the second event would prompt the agent to create a task to investigate the observation
for potential fraud, taking into account the context provided by the first event.
A prioritization agent then assesses and ranks the task, potentially initiating real-time
execution by the execution agent.
The execution agent’s role is to carry out the tasks and steps, such as analyzing the
observations for fraud in this example. It can access additional context, such as historical
transaction data and the customer’s credit card usage patterns, through techniques like

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Retrieval Augmented Generation (RAG). It may also utilize tools to access external
services, like the Google Maps API, to gather travel and distance information for the
locations where the card was used. Additionally, the agent could interact with the
customer through an app, SMS, or even initiate a phone call to aid in the analysis.
The different functional architectural blocks of an autonomous AI
agent
To build an AI agent, it is essential to understand its architecture. Here is an overview of
the same.
Users
APIs, User Interface
Agent
Tools
Prompting
Memory &
Context
Vector store
Enterprise IT Assets - Structured, Semi and Unstructured Data
LeewayHertz
The diagram presents a high-level functional architecture for autonomous agents,
comprising several key components, which will be explored next.
Agent and agent development framework

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An agent is essentially software that can be either purchased off the shelf and customized
or developed from scratch. Developing software from scratch entails creating an
abstraction layer to the foundational model APIs for various use cases, ranging from
chatbots to orchestration foundations. This process involves building a scalable execution
layer and integrating it with existing databases, external APIs, and emerging frameworks.
Alternatively, you can utilize an existing orchestration framework that offers numerous
essential features for managing and controlling LLMs. These frameworks simplify the
development and deployment of LLM-based applications, enhancing their performance
and reliability.
Several orchestration frameworks are available, with LangChain and LlamaIndex being
two of the most prominent. LangChain is a leading open-source framework designed to
assist developers in creating applications powered by language models, particularly large
language models (LLMs). It streamlines development by providing standardized
interfaces for LLM prompt management and external integrations with vector stores and
other tools. Developers can construct applications by chaining calls to LLMs and
integrating them with other tools, thereby improving efficiency and usability. The
fundamental concept of the library is that different components can be linked together to
develop more advanced use cases surrounding LLMs.
Another two most promising agent development frameworks are Microsoft Autogen and
crewAI. Microsoft’s AutoGen is a platform that facilitates the creation of applications
based on Large Language Models (LLMs) by leveraging multiple agents. These agents
can engage in iterative conversations with one another to accomplish tasks. They offer
customization options, support human involvement, and can function in diverse modes
that incorporate a mix of LLMs, API calls, and custom code.
Large Language Models
Large Language Models (LLMs) are crucial in the development of AI agents, acting as the
foundation for natural language processing and generation. The primary purpose of
incorporating LLMs into AI agents is to enable them to understand and generate human
language effectively. This allows AI agents to interpret user queries, extract information
from extensive text data, and maintain engaging conversations with users. Moreover,
LLMs provide AI agents with contextual awareness, ensuring that responses are not only
relevant but also coherent with the ongoing dialogue. As language evolves, LLMs enable
AI agents to learn from new data and adapt to changes, keeping their responses up-to-
date.
Different LLMs can be utilized depending on the specific needs of the AI agent. General-
purpose models like GPT-3 or BERT offer versatility and can be applied across a variety
of tasks, from chatbots to content generation. For more specialized applications, such as
legal or medical assistance, domain-specific LLMs trained on relevant data can provide

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more precise and pertinent responses. Additionally, organizations can develop
customized LLMs tailored to their unique requirements by training them on proprietary
data.
In summary, LLMs play a vital role in building AI agents by enabling them to understand
and generate human language, maintain context in conversations, and adapt to linguistic
changes. The choice of LLM depends on the intended application of the AI agent, with
options ranging from general-purpose to domain-specific and customized models.
Tools
In the architecture of AI agents, a key component is the ability to integrate with external
services and APIs, commonly referred to as “Tools.” These tools extend the capabilities of
agents beyond mere language processing, enabling them to access additional data and
systems to perform a wider range of tasks. For instance, an agent might use a simple tool
like a calculator for numerical operations or a more complex tool such as an API to
interact with enterprise backend services.
The integration of tools provides agents with the autonomy to choose the most
appropriate resource for a given task, whether it’s retrieving information or executing an
action. This flexibility enhances the agent’s effectiveness in completing assignments.
The ecosystem of available tools is constantly expanding, with a variety of public services
and APIs that agents can utilize. Additionally, agents can access operational data stores
or vector stores to incorporate relevant domain-specific data into their processing. For
example, an agent might use a tool that accesses a vector store based on
AstraDB/Cassandra to retrieve product documentation. Instead of relying solely on a
language model for answers about a product feature or code samples, the agent can
perform a vector search query against its own knowledge database to provide a more
accurate response.
Memory and context
Agents, by their very nature, do not retain state and thus require a mechanism for storing
information, necessitating both short-term and long-term memory layers. Consider the
example of a coding agent; without memory, it cannot recall its previous actions.
Therefore, if posed with the same question, it would invariably begin from scratch,
reprocessing the entire task sequence anew. Implementing a memory feature becomes
crucial in this context.
As the memory has the potential to rapidly expand into a vast dataset, envision it as a
memory stream filled with numerous observations pertinent to the agent’s current context,
such as logs of questions, responses, and interactions within multi-user environments.
Utilizing a vector search for retrieval, supported by a low-latency and high-performance
vector store like Astra DB, becomes an efficient solution. This approach ensures that the
agent can quickly access relevant information, enhancing its ability to respond to queries
and perform tasks more effectively.

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For an agent to effectively operate within or comprehend your specific domain context,
such as your products, industry, or enterprise knowledge, it is not feasible to rely solely on
an off-the-shelf Large Language Model (LLM).
This doesn’t necessarily mean that you need to train your own model from scratch.
However, an existing pre-trained model may require fine-tuning to adapt to your domain
context, or it may need to be supplemented with this context using techniques like
Retrieval Augmented Generation (RAG). Often, a combination of fine-tuning and RAG is
effective, especially in scenarios with stringent data privacy requirements. For instance,
you may want to avoid storing sensitive company intellectual property or customer
personally identifiable information directly in the models.
Additionally, when new context data is frequently added, or when there is a need to
optimize performance metrics such as latency and throughput, or to minimize the costs
associated with model invocation, injecting data via RAG becomes the preferred method.
This approach integrates a retrieval model over a knowledge base with the LLM through
its input prompt space, providing context that was not included in the model’s initial
training corpus.
Building an AI agent – the basic concept
In the field of artificial intelligence, an agent refers to software that can sense its
environment (such as a game world) and take actions (like a character moving and
making decisions) based on specific rules or algorithms.
Agents vary in complexity. Some, known as simple reflex agents, react solely to their
immediate perceptions, like a thermostat. Others, called goal-based agents, consider
future outcomes and act to achieve their objectives. The most sophisticated, learning
agents, can adapt their behavior based on past experiences, much like humans learning
from mistakes.
The power of agents lies in their ability to automate intricate tasks, make smart choices,
and interact with their surroundings in a way that emulates human intelligence. The
exciting part is that anyone can create these agents. By developing AI agents, you unlock
a world of potential, where you can develop systems that are not only efficient and
effective but also capable of learning, adapting, and evolving.
While more complex agents may need expert knowledge, starting with simple agents is a
great way to learn and grow in this fascinating area.
The development of autonomous agents powered by Large Language Models (LLMs) has
gained significant attention due to the rapid advancements in LLM technology. Over the
past year, numerous new technologies and frameworks have been introduced based on
this concept.
In our exploration of available options, we encountered AutoGen, an open-source agent
communication framework developed by Microsoft.

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AutoGen addresses a crucial need that many new technologies have overlooked:
enabling multiple agents to collaborate toward a shared objective. It provides essential
functionality to support the initialization and collaboration of multiple agents atop an LLM.
It facilitates one-to-one communication channels between agents and group chats
involving multiple agents. This feature was particularly crucial for our use case. However,
before delving into the specific use case let’s have an overview of our selected
framework, i.e. Autogen.
Microsoft Autogen – an overview
Conversable Agent
Agent Customization Flexible Conversation Patterns
Multi-agent Conversations
… …
…
Joint chat
…
… … …
Hierarchical chat
LeewayHertz
Microsoft’s AutoGen is a framework designed to facilitate the development of applications
utilizing Large Language Models (LLMs) through the collaboration of multiple agents.
These agents are capable of conversing iteratively to accomplish tasks, are customizable,
allow for human participation, and can operate in various modes that integrate LLMs, API
calls, and custom code.
AutoGen is built around four key concepts: Skill, Model, Agent, and Workflow.
Skill: This is akin to OpenAI’s Custom GPTs. It enables a combination of prompts
and code (e.g., accessing APIs) and can be employed by Agents to execute tasks
more efficiently and accurately, as they are curated by human experts. For instance,
generating a creative quote of the day and sending it to a Telegram bot via API
could be a skill. The LLM might excel in generating the quotes, while the action of
sending them via the Telegram API could be more effectively executed by custom
code.
Model: This refers to the configuration of any LLM that is intended for use.
Selecting the most suitable LLM for a specific task is crucial.

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Agent: This is the actual “bot” configured with the chosen Models, Skills, and a pre-
configured prompt (also known as a System Prompt) to optimally perform the
designated task(s).
Workflow: This is a comprehensive encapsulation of all the Agents required to
collaborate to complete all tasks and achieve the desired goal.
AutoGen Studio is an open-source user interface layer that overlays AutoGen,
streamlining the rapid prototyping of multi-agent solutions. It provides a user-friendly
interface for configuring and linking Skills, Models, Agents, and Workflows, eliminating the
need to manipulate configuration files and execute scripts manually.
As previously mentioned, AutoGen is a framework based on Large Language Models
(LLMs) for agent communication. It enables the creation of agents with distinct personas,
which can collaborate through one-to-one message passing or group chats, where each
agent contributes in turn.
AutoGen includes several built-in agent types with varying capabilities, such as:
User Proxy Agent: Acts as a user representative, capable of retrieving user inputs
and executing code.
Assistant Agent: Equipped with a default system message, this agent functions as
an assistant to complete tasks.
Conversable Agent: Possesses conversational skills and serves as the foundation
for both assistant and user proxy agents.
Additionally, AutoGen features experimental agents like the Compressible Agent
and GPT Assistant Agent.
While AutoGen primarily supports OpenAI LLMs like GPT 3.5 and GPT 4 for agent
creation, it can be configured to work with local or other hosted LLMs as well.
AutoGen Group Chat: Group chats in AutoGen enable multiple agents to collaborate in
a group setting. Key features include:
All agents can see the messages sent by others in the group.
The group chat continues until a termination condition is met, such as an agent sending a
termination message, the user exiting the chat, or reaching the maximum chat round
count.
A manager agent oversees message broadcasting, speaker selection, and chat
termination.
AutoGen supports four methods for selecting the next speaker in each chat round:
manual, random, round-robin, and auto (where the LLM chooses the next speaker based
on chat history).
These features make AutoGen group chat suitable for agent collaboration, but they also
present challenges in terms of controlling agent interactions within this environment.

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AutoGen for application development: Currently, AutoGen is designed for scenarios
where the user has full visibility of all internal communication between agents. Integrating
AutoGen into an application where such transparency is not desired can be challenging.
For instance, in a system where multiple agents act as sales assistants, revealing their
internal planning and strategy selection to the user may not be ideal. Additionally,
exposing users to the complexity of internal communication can be overwhelming.
Moreover, integrating an AutoGen agent system with an API poses challenges, as
AutoGen is primarily a CLI tool and lacks a consistent method for ending chat sequences
without explicit user input.
Fortunately, certain customizations supported by AutoGen can help overcome these
issues, enabling satisfactory integration with an API. The following sections will detail how
we achieved this integration.
How to build AI agents with Autogen: Essential steps
Discover the essential steps on how to build AI agents with AutoGen, a powerful tool for
creating intelligent, automated systems
Setting up AutoGen Studio
To begin using AutoGen Studio, you must first install it on your local computer. The
installation process is simple and can be completed using the pip package manager. It is
advisable to install AutoGen Studio within a conda environment to prevent any package
conflicts. Additionally, you will need to acquire an API key to access your language
models and securely authenticate with OpenAI. AutoGen can work with any Large
Language Model (LLM), including those hosted locally, such as LLAMA 2 or Mixtral, by
simply configuring API endpoints for AutoGen to interact with. For those just beginning,
utilizing OpenAI’s services is likely the most straightforward and convenient option. You
can set up your secret key on the OpenAI platform. After installing AutoGen Studio and
configuring your API key, you can initiate it via a command line. AutoGen Studio will
operate on a local server and offer a web-based interface for developing and
experimenting with applications.
Developing skills
The initial phase in constructing a multi-agent application using Autogen Studio involves
developing skills. Developing a new skill entails crafting a function to execute a particular
task. Skills are essentially functions that enable your language models to carry out
particular tasks or produce specific outputs. For example, you can create a skill to
generate images or retrieve data from a designated source. While Autogen Studio offers
a range of default skills, you also have the option to create your own tailored skills. To
develop a skill, you must describe its purpose and implement the required code in
Python. These skills will then be utilized by the agents in your application to execute
various tasks.

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Leveraging models
Autogen Studio offers the versatility to employ both locally hosted language models and
those available through Azure or OpenAI. Local models let you run multi-agent apps on
your own, without needing external services, by just setting the model’s path in your app.
Conversely, utilizing models from Azure or OpenAI necessitates the provision of your API
key for authentication purposes. A diverse selection of models is available to suit your
specific needs. Autogen Studio streamlines the integration of these models into your
application, enabling you to concentrate on developing your multi-agent workflows.
Configuring agents
In your multi-agent application, agents are the components that carry out tasks and
engage with users. With Autogen Studio, you have the capability to configure agents,
assigning them particular skills and models. For each agent, you can designate a primary
model that will be utilized by default for handling user inquiries. The roles and
responsibilities of agents can vary depending on the skills you assign to them. Autogen
Studio includes a user proxy agent that acts on behalf of the user and executes code on
the user’s system. Additionally, you have the option to create custom agents with tailored
functionalities and incorporate them into your application.
Developing workflows
In Autogen Studio, workflows outline the series of steps and interactions among agents
within your application. They coordinate the performance of tasks and regulate the
exchange of information among agents. Depending on your application’s needs, you can
develop various workflows. For instance, you might design a workflow for data
visualization in which one agent retrieves data, another creates visualizations, and a third
agent displays the outcomes. Autogen Studio offers an intuitive interface for designing
workflows and determining the sending and receiving agents for each workflow.
Leveraging Autogen playground
Autogen playground is a robust feature offered by Autogen Studio that enables you to test
and illustrate workflows. It facilitates the interactive development and execution of
workflows, allowing you to track agent activities and visualize outcomes. You can initiate
by crafting a new workflow and defining the participating agents. Autogen playground
offers pre-built sample tasks as a foundation. You can pose queries, activate particular
skills, and watch how agents collaborate to accomplish tasks. Additionally, Autogen
playground generates Python code for each task, providing you with complete control
over the implementation details.
An example of Autogen-based tour agent system

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Conversable Agent
UserProxy Agent GroupChat Manager
Assistant Agent
LeewayHertz
We’ll explore a simple tour agent system powered by Autogen. This system comprises
two Autogen Assistant Agents and a User Proxy Agent, all working together in a group
chat. Here’s a brief overview of their roles:
Tour agent: This is the primary agent responsible for replying to user queries. It
gathers necessary information before crafting a final response for the user.
Location researcher: This assistant agent aids the tour agent by conducting
location research. It utilizes function calls to query Google Maps via the Search
Engine Results Page (SERP) API, gathering details about attractions, restaurants,
accommodations, and more.
User proxy: This agent acts as a proxy for the user within the group chat,
facilitating communication between the user and the other agents.
Configuration
First, we set up a common configuration for all agents in the system. This involves
specifying the model and API key for the services we’ll be using.
Creating Assistant Agents: Next, we create the Tour Agent and Location
Researcher. The Tour Agent has a customized prompt outlining its role and
responsibilities, while the Location Researcher is equipped with a function for
searching Google Maps.
User Proxy: The User Proxy is created to handle user messages and detect when
to end a reply sequence before sending the response to the user. It plays a passive
role but is essential for managing the flow of communication.
Group Chat and manager agent: Finally, we set up a group chat and a manager
agent to enable collaboration among the agents. The group chat allows for a
structured conversation, while the manager ensures that the conversation flows
smoothly and ends appropriately.

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In summary, this Autogen-based tour agent system demonstrates how multiple agents
can work together to provide a comprehensive service, from handling user queries to
conducting research and managing communication.
Benefits of Autogen
Enhances LLM workflows: AutoGen streamlines the management, refinement,
and automation of large language model workflows, making them more efficient.
Adaptable and interactive agents: The platform provides agents that are both
customizable and capable of engaging in dialogue, utilizing the power of
sophisticated LLMs like GPT-4.
Human and tool integration: AutoGen overcomes the limitations of LLMs by
enabling integration with human input and various tools, allowing for collaborative
conversations among multiple agents.
User-friendly and modular approach: The framework simplifies the creation of
complex multi-agent systems, offering a modular design that allows for easy reuse
and combination of agents.
Dramatic reduction in coding effort: Utilizing AutoGen can result in a significant
decrease in coding effort, potentially reducing it by more than four times.
Flexible agent functionality: Agents can be configured to employ LLMs, human
input, tools, or a mix of these elements, providing a broad spectrum of
functionalities.
Smooth user interaction: AutoGen facilitates smooth user interaction, allowing
users to easily join or leave a chat through an agent, enhancing the user
experience.
Dynamic group chat support: The platform supports dynamic group chats
involving multiple agents, broadening the scope for collaborative endeavors.
Community-driven open-source project: As an open-source initiative, AutoGen
encourages contributions from a diverse community, fostering ongoing development
and innovation.
How LeewayHertz can help you build AI agents
LeewayHertz understands that AI agents are not merely technological advancements;
they are transforming the future of businesses, lifestyles, and societal interactions. AI
agents, from advanced virtual assistants and interactive chatbots to autonomous
vehicles, are reshaping automation, decision-making, and customer engagement. In
today’s fast-paced digital environment, adopting these intelligent entities is crucial for
businesses seeking to excel and maintain a competitive edge.
As a leader in AI development, LeewayHertz empowers businesses across various
sectors to harness the power of AI agents. Our expertise in AI and machine learning
solutions enables us to enhance your business by integrating state-of-the-art AI agents

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into your technology ecosystem. Our dedicated team of AI specialists is committed to
delivering custom AI agents that seamlessly align with your business goals, boosting
operational efficiency, reducing costs, and fostering innovation.
As an experienced AI development company, LeewayHertz also leverages tools like
AutoGen Studio and CrewAI for AI agent development, along with other approaches
offering a comprehensive and collaborative approach. Here are some of the AI agent
development services that we follow as part of our AI agent development:
1. Strategic consultation: We provide strategic consultation services, assisting you in
understanding the potential of AI agents for your business, identifying integration
opportunities, and developing effective digital transformation strategies.
2. Custom AI agent development: Specializing in the development of custom AI
agents, we utilize AutoGen Studio for rapid prototyping and CrewAI for orchestrating
collaborative agents. This ensures that your AI agents are tailored to your business
needs and challenges, streamlining processes and achieving operational objectives
with precision.
3. Seamless integration: Our team excels in integrating AI agents into your existing
systems using AutoGen Studio and CrewAI. This ensures smooth interoperability
and minimal disruption while maximizing the benefits of intelligent automation and
data-driven insights.
4. Continuous support and optimization: Our commitment extends beyond
deployment. We offer ongoing support, monitoring, and optimization services to
ensure that your AI agents remain cutting-edge, delivering optimal performance and
staying ahead of market trends.
In a future where AI agents are crucial for competitive advantage, LeewayHertz stands as
your reliable technology partner, leveraging AutoGen Studio and CrewAI to develop and
integrate AI agents that drive your business forward.
Endnote
As we conclude our exploration of building AI agents, it’s clear that these intelligent
systems hold immense potential to transform various aspects of our lives and industries.
From enhancing customer experiences with personalized interactions to streamlining
complex operations and making informed decisions, AI agents are at the forefront of
technological innovation.
The journey of creating an AI agent is both challenging and rewarding, requiring a
thoughtful approach to setting objectives, selecting the right technology stack, designing a
robust architecture, and developing core capabilities. Training, testing, and continuously
improving the agent are crucial steps to ensure its effectiveness and adaptability.
Moreover, deploying and monitoring the AI agent in real-world scenarios is a critical
phase where the theory meets practice, and the true value of the agent is realized.
Ensuring security and privacy in AI agent development is not just a legal requirement but

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a moral imperative to build trust and protect individuals’ rights.
As we look to the future, the possibilities for AI agents are boundless. With advancements
in AI and machine learning, these agents will become even more intelligent, autonomous,
and integrated into our daily lives. However, with great power comes great responsibility.
It is essential to build AI agents ethically, considering their impact on society, the
economy, and the environment.
In summary, building an AI agent is a journey of innovation, creativity, and responsibility.
By following the steps outlined in this article and staying abreast of the latest
developments in AI, you can create intelligent systems that not only meet the needs of
today but also pave the way for a smarter, more efficient, and more connected world
tomorrow.
Transform your business with intelligent AI agents: Partner with LeewayHertz AI experts
for advanced AI agent development and stay ahead in the competition!