Ever used a customer support chatbot or virtual assistant for filing a service request or asked ChatGPT to write an email or explain a concept, as if to a 5-year-old?
If you have, you have already interacted with an AI agent. AI agents are software programs having the agency to act on behalf of a system, business, or user.
Today, businesses build AI agents to streamline complex workflows, solve problems using logic, or answer user queries. AI agents can plan, collaborate, automate, think, perform, and even mimic a human brain.
How to build and train your own AI agent
AI agents can empower individuals by bringing unforeseen possibilities within reach. You could sort your research, Excel sheets, presentations, work management, client responses, and so many other tasks, provided you were able to build one for yourself.
But is it possible to develop an AI agent without any coding expertise?
This article discusses how to create AI agents step-by-step without any coding, and the platforms you can use to build and train AI agents.
What Is an AI Agent and Why Everyone’s Talking About It?
AI agents are software tools that automate workflows by performing tasks, making decisions, and interacting with their environment in an intelligent and rational way.
These tools use AI to learn, act, and adapt based on the training data, real-time feedback, and new information that becomes available as a result of changing conditions.
An AI agent uses a combination of machine learning, natural language processing, advanced algorithms, and decision-making processes to perform tasks intelligently and independently. The agent continually learns and improves its performance based on the feedback, experience, and new data inputs.
An AI agent typically has three distinct components:
Architecture and algorithm: AI agents process data using predefined architecture and algorithms help them to make informed decisions and learn from experience.
Workflow: Workflow is defined based on the goal or objective set by the builder and the plan of action created by the agent to execute the necessary steps.
Autonomous processes: It automates repetitive tasks and processes involving zero human interaction.
The past few months have seen Google, Microsoft, OpenAI, and other big names invest heavily in setting up software libraries and frameworks to power Large Language models (LLMs), which form the core of these agents.
AI agents can be used in different industries:
Software development: for code reviews, testing security vulnerabilities in smart contract codes,
Healthcare: accelerate healthcare diagnosis and analysis,
Manufacturing and logistics: optimise manufacturing processes, warehouse management
Financial services: enhance customer experience in financial services.
Retail and ecommerce: optimise supply chains, manage inventory, personalise marketing campaigns, and the list goes on.
How to Get Started With Building Your Own AI Agent?
Here’s a beginner-friendly, step-by-step process on how to create AI agents without using any code:
#1 Define the Scope and Purpose of Your Agent
An AI agent automates and performs complex tasks autonomously based on the predefined goals. If you are building an AI agent on your own, you need to decide the purpose and scope of tasks the agent must perform to attain a predefined set of objectives.
You must answer a few questions to be clear about what role your AI agent would have before building the agent:
Q: Do you want to build the agent software from scratch or use an existing platform to build one?
A: If you have no coding skills, it is best to choose a platform that allows you to develop an AI agent using prompts or instructions. These kinds of AI agents are called hosted agents.
If you are a developer proficient in coding, you can build one from scratch and train it yourself. These are called self-trained AI agents.
Q: What do you want to build an AI agent for?
A: There can be endless use cases for an AI agent. Define your use case. What purpose will your AI agent serve? Will it help in handling customer queries, or help the marketing team in designing and implementing the next social media campaign, or help your online customers by offering personalised shopping advice?
Q: What kind of autonomy would your AI agent possess?
A: Next, list down the tasks your software would solve or perform. Whether it would be working on its own, or as a part of an architecture with multiple AI agents performing specific tasks and reporting to the manager agent. The level of autonomy and the kind of input data it would require are some other parameters.
Q: What type of AI agent would it be?
A: Would the agent be reactive or goal-based? Will it be a limited memory or learning agent? You should also consider the ethical or regulatory compliance it would follow to be in sync with industry standards.
Here’s an article by IBM explaining the types of AI agents in detail.
Q: Who would it serve?
A: Determine the target audience your agent would be serving. Will it serve healthcare professionals, sportspersons, students, customers, or business prospects? Each kind of audience will have a different set of expectations, language and jargon comfort, and level of familiarity with tech.
A chat support bot would ask customer FAQs, and speed and automate repetitive queries, an email sorter would detect spam, filter and categorise emails based on their urgency and importance, and so on.
Once you have answered these questions, you are ready for the next steps.
#2 Choose Your Launch Path: No-Code or Full Control
As discussed, there can be two ways to build your AI agent: code or no code.
No-code platforms: No-code platforms like Autogen, AgentGPT, CrewAI, and LangChain are good options for non-developers interested in building AI agents. Low-code platforms such as Einstein from Salesforce offer better customization while avoiding the need to write full code and deployment. There are also specialised AI agent development platforms, such as IBM’s Watson, which is a conversational AI, or SquadStack or Ada, which are AI agents for customer service automation.
Custom builds (for techies): Vertex AI from Google and SageMaker from Amazon are full-on code-centric platforms built for developers seeking the flexibility and control.Experienced developers can build their custom-built programs from scratch on self-hosted platforms as well.
Here’s a comparison table charting the pros and cons of code and no-code platforms:
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#3 Choose Tools & Platforms For Building AI Agents
Today, developers can use a wide variety of platforms to build AI agents. These tools and platforms have in-built autonomous systems, planning and tool integration capabilities, visual workflow tools, developer-centric libraries, external tools and APIs, etc., to assist developers.
Here’s a comparative analysis of a few popular AI agent builder tools and platforms:
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#4 Prepare Data to Fine-tune your Agent
Your AI agent program will learn from the data. Choosing what kind of data would be used to fine-tune and train the AI model forms the crux of how good your AI agent would be.
The training data should be clean or unbiased, accurate and relevant, and of high quality. This would ensure your software program doesn’t learn wrong things, or makes mistakes, and becomes rogue.
To ensure your AI agent processes user inputs properly and understands them well, data can include:
Text transcripts of previous interactions with customers, professionals, or whoever the target audience is. These transcripts could be chat logs, support tickets, etc., to help your AI model learn to expect the kind of conversations.
Voice recordings are another way to feed your AI agent data on different kinds of accents, speech patterns, intonations, etc. These recordings would prepare the model on how to respond to spoken commands and inquiries.
Other kinds of interactions from user logs can help AI models to gather insights on user behaviours, common query/response patterns, etc.
An important step in preparation of data sets for training your AI model includes cleaning it and removing/correcting any irrelevant or incorrect data. The datasets can be purchased from external providers, be publicly available, or consist of internal data records. Add tags or metadata to data to explain what each piece of data represents. This process is called labelling, which helps AI to understand the context and purpose of the data.
#5 Training and Fine-Tuning
Once your datasets are cleaned and labelled, you can get started with training the AI agent program. Training refers to making AI agents equipped with the knowledge and abilities to perform tasks, think, and make decisions on their own.
In no-code platforms, you are required to provide sample conversations and desired responses to make your AI agent learn specific behaviours, its responses, and the tasks to be performed.
In custom-build AI agents, training is a multi-step process. You are required to collect and prepare data, select the model, train, and evaluate it, and finally deploy the model.
You may also opt for pre-trained models like GPT and BERT to train your AI agent. This allows for faster development and improved performance. Fine-tuning involves training the model on specific datasets. If you are a beginner, you can go for pre-trained models or choose a no-code platform to lessen the complexity and time involved. You can watch YouTube tutorials on how to build one to get started. Here’s a good example (Autogen Tutorial).
An important consideration while training your AI agent is choosing the right kind of machine learning model. While neural networks are great at processing large amounts of data and recognising patterns, reinforcement learning models learn through trial and error and improve performance over time.
Despite your most conscious effort in training your AI model the right way, challenges such as bias, overfitting, and underperformance remain.
#6 How to Test and Monitor Your AI Agent
It is important to test AI agents before final deployment to avoid any errors or performance issues in meeting the expected goals. Begin by running the AI agent through a series of test tasks or queries, and monitor its responses.
This step examines how accurately and efficiently your agent tool will work when deployed. By stimulating real-world case scenarios, you can also put your tool under stress test and see whether the interactions are accurate and smooth.
You can use techniques like cross-validation to ensure your AI agent doesn’t suffer from overfitting and underperformance issues. Cross-validation is when you rotate the data used for training to see whether the model generalises the responses well.
After deployment, it is equally essential to monitor your agent continuously to ensure it adapts and stays relevant to the changing conditions. Implement a feedback loop that ensures your AI agent learns and improves on the job.
Integrations
Even if you have the best AI agent, not integrating it properly into existing systems can undermine its performance. Plan the integration process in time and work on technical challenges such that your AI agent seamlessly communicates with your other platforms.
Future Outlook
AI agents are all the rage today, and companies are increasingly using them to automate processes and make human intervention more efficient. The crypto sector has gone a step ahead and built AI agents that can handle your funds on behalf of you, make trades, search for DeFi opportunities, and whatnot.
Creating an AI agent might seem like a daunting task, but it is not. We tried breaking down the entire process into steps for you to follow. Ensure choosing the right data set, model, and tools for building, training, and testing your AI model.
As they become more accurate, AI agents will become a non-negotiable deal for all businesses. Be an early mover. Use your impeccable skills to build the next big AI agent!
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