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Building AI agents is one of the coolest things we do at Azumo. These systems change how businesses operate by automating tasks, improving decisions, and enhancing customer experiences.
When we create an AI agent, we take the time to understand the business needs and design a solution that adds real value.
Whatever it is, like a simple AI chatbot, a data assistant, or something more complex in the backend, we build systems that work seamlessly.
So, let’s walk through how to build an AI agent the Azumo way!
What Are AI Agents?
AI agents perform tasks independently or with minimal human guidance. They’re great at doing repetitive jobs and connecting with other tools to simplify everything.
At Azumo, we specialize in two types of AI agents:
- Workflow-Based Agents: They are virtual personal assistants who follow a fixed sequence to do something. AI agents excel in handling repetitive tasks or providing consistent responses. You instruct them, and they follow through.
- Autonomous Agents: These agents offer greater flexibility. They can make decisions independently and adapt as new information comes in. So, for example, if they’re analyzing customer feedback, they can adjust their response or suggestion based on what they learn without needing anyone to tell them what to do every time.
However, AI agents don’t always interact with users directly. For instance, when building an AI agent for document processing, the agent may not “talk” to anyone at all. It just receives documents, processes them, and returns the relevant data. These agents can even integrate into systems like Jira or Slack.
On the other hand, front-end agents interact directly with the user. These agents, like chatbots, respond to customers' inquiries, provide recommendations, and guide the user through processes in real time.
Both front-end and back-end agents are important in business operations, based on the client's requirements and needs.
Key Components of an AI Agent
Several key factors are required to make a top-performing AI agent function properly.
Those features separate an average script from a top-performing AI agent. Let's see how an AI agent operates:
- Memory: Think of memory as the agent's brain. It stores important information, past experiences, and things it has learned. Memory exists in different types: short-term memory (for the task at hand), long-term memory (for knowledge it accumulates over time), and global memory (shared between agents to help each other).
- Planning: To achieve key objectives, an AI agent breaks them down into smaller tasks using planning algorithms. These algorithms help the agent map out a sequence of actions, relying on its memory and past experiences to optimize each step for better efficiency.
- Tool Integration: For the agent to perform something besides talk, we give it access to tools like APIs, databases, or software interfaces.
- Sensing: Just as humans perceive what is happening around them, AI agents must sense their environment. This could be reading text, scanning images, listening to sound, or using sensors to track location or network data. Sensing their surroundings, agents can respond to changing conditions in real time and make more informed decisions.
- Model(s): Models are the brains behind how the agent understands language and solves problems. Whether a large model like GPT or something more specialized, these models help the agent interpret natural language, think through complex issues, and make decisions based on the data they’re working with.
- Goals: All AI agents have some goal that they're working towards. That's what drives all of their behavior. If it's answering questions, summarizing data, or suggesting alternatives, whatever they do is ultimately to get themselves closer to achieving that goal.
- Environment: The environment is where the AI agent operates. It can be a virtual environment, like a website or an application, or a physical environment, like a robot that moves around. The agent operates on its environment, using its tools and models to reach its goal.
Multi-agent Systems (MAS)
A multi-agent System (MAS) is simply a group of expert AI agents working together to do gigantic tasks that could not be done by one agent alone. A MAS can do better, be more flexible, and keep things easier through this division.
In an MAS, each agent has its own work. All of them collectively function to reach a common goal.
For example, if it is a content creation project, one agent can do research, another can write the content, and the third can handle editing and feedback.
All these agents work under the guidance of an orchestrator agent, working together as a team toward the client's goal.
And now that you have understood the most critical factors of AI agents and how MAS functions, it's time to give you a walk-through of our process.
How We Build an AI Agent from the Ground Up
1. We Understand What the Client Needs
When a client approaches us with a request for an AI agent, the first thing we always ask is, “What do you want the agent to do?” It sounds simple, but it is absolutely fundamental.
AI agents can do different things: some are simple, like a chatbot answering queries, and some are more complex, like an agent that analyzes data and makes suggestions.
For example, if we are working with an SEO firm, they might want an AI agent that helps automate the content analysis or helps track SEO performance.
If we know the purpose, we can determine the most suitable agent for their needs and how to effectively implement it.
In most cases, clients are initially not clear about the requirements. We ask targeted questions to clarify their business goals and needs. This allows us to gather the necessary information to create a solution for their needs.
Creating AI agents is definitely a team effort. As we develop the agent, we'll move back and forth with the client. This means asking deeper questions about their company, gathering more specific data, and understanding their workflows and processes in greater detail.
They may start with a vague idea but request more features or changes once they see the AI agent's capabilities. So, we're constantly adjusting the agent until it's precisely what the client needs.
We don’t just collect the requirements and deliver the product; we work with the client throughout the development process, refining the agent based on their feedback.
2. We Build the Knowledge Base
To ensure our AI agent delivers optimal performance, we gather the right information to support its training. This involves:
- Company-specific information: We obtain primary documents, guidelines, and internal guides that describe your company's products, processes, and policies. This enables the AI agent to learn about the context through which it interacts with the users.
- Product or service information: We give the agent detailed product information, technical specifications, and documentation, which allows the agent to respond to product-related questions correctly.
- Historical interaction data: We analyze past customer interactions, such as support tickets or email conversations, to identify common queries, recurring issues, and frequently asked questions. This enables the agent to learn from real user behaviors.
We utilize Retrieval Augmented Generation (RAG) to make sure the agent pulls in the right information when it needs to. RAG lets us connect the agent to a knowledge base, so it doesn't give pre-written answers but valuable, accurate answers based on what it's been informed of.
Once we have gathered all this data, we move into the preparation phase, where we clean and organize the data for training. This is done by eliminating irrelevant data, fixing errors, and aligning all data sources.
For example, we update product information to contain the latest information and remove incomplete or stale interactions. This gives the AI agent good data for precise and effective outputs.
3. We Add Integrations and Tools
With the information now accessible to the AI agent, we equip it with tools to execute without glitches. These may be APIs to call third-party services or specific functions like creating a report, importing from a spreadsheet, or even emailing.
For example, a financial planner agent might need an API to access the latest market data or a calculator to perform financial calculations. These tools give the agent a way to do something rather than simply to respond.
It's not necessarily answering questions; it's about performing tasks and taking actions, like creating PDFs from data or reporting.
Each tool is customized based on the agent's requirement, e.g., an Excel tool to handle data or a search tool to retrieve data from the internet.
4. We Choose the Right Tech Stack
Regarding the tech we use to develop AI agents, there is no one-size-fits-all solution. We utilize different AI agent frameworks depending on what the client needs.
For workflow and pipelines, we love using LangChain and LandGraph. These tools allow us to develop processes to automate the agent's work and ensure it works correctly.
On the backend, we mainly use Python and FastAPI, which are robust and flexible enough to handle the heavy work of AI. But we're not tied to any specific tech stack. The most important thing is what is best for the client's project.
The tech stack will be adjusted based on the client's requirements. This could involve utilizing a VectorStore to manage embeddings or using a series of different LLMs so that they can work together as a more sophisticated AI system.
5. We Test and Debug the AI Agent
Once the agent is operating, we must test and debug it to ensure it operates correctly. Since AI agents will interact with multiple systems at once, things can get complicated. During testing, we run various scenarios to identify potential failures or inefficiencies. Debugging involves tracking down any issues in the agent's interactions, refining the code, and ensuring it can seamlessly communicate with all integrated systems.
That's why we use tools like Langsmith, where we can see everything that the agent is doing step by step to make sure that it is functioning as it should.
The actual challenge is when the agent is production-ready. The agent must be able to deal with real-world challenges and operate smoothly 24/7.
We ensure it can scale, handle unexpected problems, and play nicely with the client's systems. Scaling is always in the back of our minds because what might work in testing can catch real-world complexity once the system is live.
6. We Provide Ongoing Maintenance and Updates
Even after an AI agent is released, there will always be more to do. Like any software, agents need maintenance. Whether it involves debugging, adding new features, or optimizing performance, we always keep the agent up-to-date.
Also, if the agent must learn through interactions with it (e.g., learning its responses to improve based on real-world use), we can set that up.
But not all agents can be set up to learn automatically, so we'll need to develop a learning environment if necessary. That way, the agent gets to keep improving as business requirements continue to change.
Wrapping Up: Build Your AI Agent with Azumo
So, to build AI agents, we understand what the client needs and use the right tools and technology to deliver a solution that works.
Whether it's an agent responding to customer inquiries, processing data, or helping automate a business process, our AI agent development services are all about one thing: creating an agent that provides real value and addresses a specific problem.
AI agents, like any other software, must be built, tested, maintained, and updated. However, once built, they can help businesses automate processes, enhance efficiency, and enable companies to get things done faster and better.
If you're ready to bring your AI agent to life or want to explore how it can transform your business, reach out to Azumo today. We’ll work closely with you to design a solution that’s as unique as your business. Let’s build something amazing together!