Informative
Feb 26, 2025
Key Skills to Build AI Agents
AI agents are going to change the world.
They are the next wave of automation that will replace human beings everywhere in the workforce.
If you know Python, the basics of AI and ML, have worked with agentic frameworks, and know how to use APIs and connect to LLMs:
Congratulations - you know most of what is necessary to build AI agents.
In today's tech-driven world, you are well-positioned to construct agentic AIs that can replace humans.
But I’ll let you know a little secret - one can also build AI agents without much prior knowledge.
The no-code tools of today are democratizing the tech landscape.
To fully grasp the world-changing nature of this opportunity, make sure you read to the end of the article!
But first:
What is an AI Agent?
An AI agent is a software bot that autonomously perceives its environment, evaluates the action from the input environment conditions, interacts with APIs to integrate powerful ML and AI capabilities, and takes the best action for the situation, all without human supervision.
They can be used to automate crypto trading, stock trading, portfolio allocation, industry real-world scenarios, cybersecurity, supply chain optimization, and many more domains.
Their impact is universal.
There are many differences, and they can be best understood when comprehensively summarized in a table.
Traditional ML Software versus AI Bot
The Skillset of an Agentic AI Expert
While today, even beginners can get started on AI agents with no-code tools, traditionally, the skills required have been:
Programming Languages
AI Agents have been built with Python, R, C++, Java, Golang, Rust, and many other languages.
Python is by far the most popular.
It is extremely easy to learn and the ecosystem of open-source libraries is massive and mature.
The existing libraries in Python are the largest available worldwide.
Data Analysis & ML
While AI Agents are a brand-new concept, the same rules as standard ML apply.
One needs:
Data Preprocessing
Data Cleaning
Model Building
Model Testing and Verification
Model Deployment
Model Monitoring and Maintenance.
Exploratory Data Analysis (EDA) still applies and is still perhaps the most important step.
Of course, one needs to know the basic ML algorithms like:
Neural Networks
Decision Trees
Support Vector Machines
Ensembling
Clustering
Dimensionality Reduction
and many more.
AI Frameworks & Libraries
Deep learning libraries like TensorFlow, Keras, and PyTorch greatly simplify agent development.
They provide the basic components so that the AI developer can simply assemble them with code.
Problem-Solving & Algorithm Design
One needs to match the algorithm to the problem.
Some of the common algorithms for agents include: Uninformed Search or Blind Search like DFS or BFS, Heuristic Search like Greedy Search or Hil Climbing, Reinforcement Learning like Q-Learning, DQNs, SARSA, or other algorithms like Evolutionary Computation, Monte-Carlo Methods, and Temporal Difference Learning among others.
Choosing the correct algorithm is one of the most important steps in creating an effective AI agent.
Understanding of APIs & Integration
API integration is very important.
Knowledge of Flask, RESTful APIs, FastAPI, and GraphQL is very helpful here.
You also need to know how to integrate the APIs of LLMs like Claude and ChatGPT and maintain usage limits.
The more tokens you can save, the less money will be drained from your employer’s pockets.
Ethical AI & Security Considerations
Decision-making transparency and fairness are important for ethical AI.
Security is another critical component. Some basic best practices involve hashed passwords and API keys, minimize and anonymize data collection, secure data transmissions, ensure compliance with GDPR and other regulations, and many, many more.
Resources to Learn AI Agent Development
Elements of AI (University of Helsinki): A free online course that provides a gentle, conceptual introduction to AI. Covers key concepts without requiring programming. Good for building a solid foundational understanding. https://www.elementsofai.com/
Crash Course AI (YouTube): A visually engaging video series that explains core AI concepts in an accessible way. https://www.youtube.com/playlist?list=PL8dPuuaLjXtNlUrzyH5r6jN9qAAs0AckM
David Silver's Reinforcement Learning Course (UCL): A classic, highly regarded course on reinforcement learning. The videos are available on YouTube. https://www.davidsilver.uk/teaching/
Coursera: Coursera offers various courses on AI Agents and reinforcement learning, ranging from introductory to advanced. These courses are excellent, and all of them can be audited for free!
Communities and Online Forums
The are a huge number of communities and forums available online for you to network, find support, and clear doubts while building AI agents.
Remember to stick to the community rules.
Some of the best forums are:
Reddit:
r/MachineLearning: The largest and most active machine learning subreddit. While broad, it has frequent discussions on AI agents, reinforcement learning, and related topics. Use the search function effectively to find agent-specific threads.
r/reinforcementlearning: A dedicated subreddit specifically for reinforcement learning, a core area for AI agents. Excellent for asking technical questions and discussing RL algorithms.
r/artificialintelligence: A more general AI subreddit, but often features discussions relevant to agents.
Stack Overflow: The go-to Q&A site for programming problems. Use tags like reinforcement learning, artificial intelligence, OpenAI-Gym, deep learning, and Python (or your chosen language) to find relevant questions and answers.
Stack Exchange (AI): A dedicated Stack Exchange site specifically for Artificial Intelligence. More focused than Stack Overflow on conceptual and theoretical questions. https://ai.stackexchange.com/
There are many more options for online forums, but this is a good start. Remember the following guidelines:
Search Before Asking: Use the forum or subreddit's search function to see if your question has already been answered. This will save everyone time.
Be Specific and Clear: When asking questions, provide detailed context, including the specific problem you're facing, the code you've tried, and any error messages you're receiving. A well-formulated question is much more likely to get a helpful answer.
Follow Community Guidelines: Read the rules and guidelines of each forum or subreddit before posting.
Be Respectful and Courteous: Maintain a professional and respectful tone in all interactions.
No-Code AI Agent Tools
You can go the long way or the short way.
If you only need to build basic AI Agents, then there are several no-code solutions available online that allow you to build AI Agents with Drag-and-Drop Interfaces.
The prominent ones are:
Botify.cloud: A no-code platform for building and deploying AI-powered chatbots and virtual assistants for various business applications.
Cognigy.AI: An enterprise-grade conversational AI platform for building, deploying, and managing AI-powered virtual agents across multiple channels.
Dialogflow (Google Cloud): A natural language understanding platform from Google for building conversational interfaces (chatbots, voice assistants) that integrate with various services.
IBM Watson Assistant: An AI platform from IBM for building and deploying virtual assistants and chatbots that can understand natural language and automate interactions.
Landbot: A no-code chatbot builder focused on creating engaging, interactive conversational experiences on websites and messaging apps.
Botiify.cloudBotify vs. Its Competitors & Alternatives | Botify Cloud | Botify Updates
Challenges in AI Agent Development
AI Agents can be both simple and complex.
Either way, there are some challenges that you will always face, which include:
Data Quality Issues
As always, data is your most important component.
Unconscious mistakes and common data biases can have a huge effect on your AI Agent.
Complexity of AI Algorithms
It is important to have an intelligent agent.
However, it is equally important to be able to explain the agent’s decisions.
In mission-critical sectors, explainability and transparency become critical.
That is why one must balance simplicity and complexity while choosing your AI algorithm.
Integration Issues
After building AI agents, you may run into several issues when integrating these AI agents into real-world scenarios.
These include:
Lack of Standardization: Disparate silos can make AI agentic development a challenge in real-world systems.
API Rate Limits: When using LLMs as a backend, automated agents may exceed the rate limits for queries on an everyday basis, especially when it comes to real-time verticals like crypto trading.
Unexpected Behavior: Due to mistakes in the data or AI algorithm complexity, agents may behave in unexpected ways.
Legacy Systems: Working with legacy systems is one of the biggest challenges for every AI engineer.
There are many more, but we can stop with these for the sake of brevity.
Building Your First AI Agent
While getting started with AI agents, you can follow up on the skillset section of the blog by googling the words you do not know and learning the tools for yourself.
That could take anywhere between 6 months to a year, depending upon the time you can dedicate.
If you go down that road, following the resources section would be helpful for your progress.
However, there is a much easier path for beginners to follow.
No-code tools for building AI Agents convert a one-year journey to a one-week journey.
To go in-depth into the steps to build your first AI Agent, I suggest you go through the following article:
How To Build AI Agents For Beginners (Without Coding) | Botify Updates
This article goes into great depth into how to build your first AI agent using a no-code tool like Botify.cloud.
This platform makes it very simple for you to create your first profitable AI Agentic bot in a sector as complex as crypto trading.
As time goes by, the no-code revolution will one day catch up to the complexities that can be achieved by customized code solutions.
Or: the time will come when we can tell the AI to build what we need in English, and the AI will build it perfectly.
I believe that time will come soon.
As Andrej Karpathy famously tweeted:
“The most popular programming language of the future will be English.”
Wrapping Up
In the past, one needed to be an expert in numerous technologies to build an AI Agent.
You needed Python, Machine Learning, Reinforcement Learning, API Integration, and many more components to build an intelligent AI agent.
That time has passed.
Now we can build complex AI agents with simple no-code tools.
And with the choices available, the possibilities are limitless.
For automated crypto trading agents, we strongly encourage you to check out Botify.cloud.
Botify.cloud is an all-in-one AI agent marketplace with numerous opportunities for monetization.
Start learning with the link below and get started with your no-code AI agent creator journey today!
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