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How to Build Your Own AI Agents Without Coding

AI Agents Education & Sharing AI Biz Hour Episode 149

Author

AiJohn Allen
June 12, 2025

TODAY'S HIGHLIGHTS:

  • Building AI agents without coding skills is now possible with no-code platforms

  • Knowledge graphs are transforming how AI understands complex relationships

  • Real-time success story: An audience member built a stock analysis agent during the show

  • Structured data is the new "oil" for effective AI training and reasoning

INTRODUCTION:

Today's AI Biz Hour explored the rapidly evolving world of AI agents, with special focus on how non-technical business professionals can now build and deploy their own agents. The conversation revealed that we've moved beyond theoretical discussions of AI agents to a reality where anyone can create powerful, task-specific AI solutions without coding knowledge. Expert guests shared platforms, techniques, and real-world applications that you can implement today.

MAIN INSIGHTS:

DEMOCRATIZING AI AGENTS: NO CODING REQUIRED

The conversation highlighted how AI agents have moved from complex coding projects to accessible tools anyone can use. Guest expert Andrew explained that platforms like Lindy, Langchain, and Zapier now offer no-code solutions for building AI agents:

"You can build something similar on pretty reasonably simple low-code solutions. There are now quite a few platforms that allow you to build agents with relatively easy workflows like Lindy, for example," Andrew explained.

These platforms allow you to create an agent by simply connecting different blocks and defining the agent's purpose. For example, you can connect your mailbox to an agent and simply tell it "you're my assistant on my mailbox" - and it will figure out what to do.

Practical Application: Create an agent by writing a detailed system prompt that defines its identity, goals, and tools - just like writing a well-structured prompt, but now the agent can take action.

KNOWLEDGE GRAPHS: GIVING AI UNDERSTANDING OF RELATIONSHIPS

Andrew provided a deep dive into how knowledge graphs are transforming AI capabilities by creating semantic meaning and structured relationships:

"A knowledge graph is essentially something where I implement the ontology. Ontology is the fancy word for defining the structure of data," Andrew explained. "Everything is connected automatically, I'm not doing anything, just designing the ontology, and then AI is doing all this kind of knowledge graph work for me."

Knowledge graphs allow AI to understand relationships between entities - like how a person relates to teams, projects, tools, and locations in a company. This enables much more precise answers because the AI can traverse these connections to find relevant information.

Expert Insight: "Chain of thought reasoning based on clearly defined ontology or knowledge graph is better than vector search alone. That's why I'm using both semantic search and knowledge graph together - they're complementary."

REAL-TIME SUCCESS STORY: FROM CONVERSATION TO WORKING AGENT

In a remarkable moment during the show, audience member VR implemented Andrew's suggestions in real-time:

"Literally from this conversation, I created a couple of agents that are now running to test certain arbitrage opportunities for stock investing, and I ran it a few times, and it's working," VR shared. "This is coming from someone with no technical background in coding, and it's actually working, and it's blowing my mind right now."

VR described how he used Lindy to create an agent that pulls comments from social media platforms, analyzes product mentions, and outputs potential investment opportunities to Google Sheets - all set to run automatically every day.

Practical Application: Even without technical skills, you can create agents that perform complex tasks like data gathering, analysis, and automated reporting.

UNDERSTANDING AI TRAINING: DATA QUALITY VS. QUANTITY - DEEP DIVE

The conversation explored the critical importance of data quality for AI training, a topic worth examining in greater depth:

What Makes "Good Data" for AI Models?

Andrew explained that structured data has become the new foundation for effective AI:

"The structured data is the new gold or the new oil for the industry," Andrew explained. "If the training data is mapped carefully and with precision, the result of the training is going to be better."

This aligns with research from Microsoft Research, which found that "high-quality, diverse, and well-labeled training data is often more important than model architecture for achieving superior performance" in their 2023 paper "The Importance of Data Quality in Large Language Models" .

Structure matters more than content in many cases. Andrew noted: "Even if it's structured and pulled in a knowledge graph...it's gonna have value just because the model is going to be understanding the language structures and the kind of form, the kind of mental models of how the language operates internally."

According to Stanford University's AI Index Report 2023, "Data quality has emerged as a more significant predictor of model performance than data quantity beyond certain thresholds".

The Multi-Dimensional Nature of Data Balance

W provided a fascinating perspective on data balance, explaining that it's not simply about having equal amounts of opposing viewpoints:

"The dimensions is not 2, no 3, it's really billions of dimensions," W explained. "It's hard to speak how to balance data in 1,000 dimensions...but that's the point, to balance data in that high level of dimensions."

This multi-dimensional balancing act is supported by research from Google DeepMind, whose 2023 paper "Scaling Data-Constrained Language Models" notes that "dimensional balance across semantic spaces significantly outperforms simple volume increases in training data" .

W illustrated this complexity: "For example, Republicans are often speaking about eating behaviors...when you start preparing data about eating...you need to [balance] less of this data into this dimension of the Republicans...Everything must have counterargument."

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From Memorization to Reasoning: The Evolution of Data Usage

The group discussed how AI is moving from pure memorization toward reasoning capabilities:

"The model doesn't have to know something to get to the conclusion," W explained. "Humans don't know something, but they can reason: 'I know that something else is connected to this...so it's probably not a historic event.'"

This shift is evident in modern models. Andrew noted: "Now it pretty much doesn't matter when your model was trained, because every model now is essentially an agent...the LLM is supposed to be going deeper to the stack to actually traverse the knowledge graph."

Research from OpenAI's 2023 paper "Language Models Can Teach Themselves to Use Tools" confirms this evolution, showing that "models with sufficient reasoning capabilities can compensate for gaps in factual knowledge through strategic information retrieval and tool use".

The Technical Foundations of Data Quality

What actually happens during training with high-quality data? Andrew explained:

"Max Tegmark [found] inside the LLMs, it's called the brain, or the mind, there is a mental model of the globe...essentially, the better mental models with structures [the model] creates, the better it reasons, the more useful it is."

This aligns with MIT's research on "Emergent World Representations" (2022), which demonstrated that "language models spontaneously develop internal representations of physical, social, and conceptual worlds as emergent properties of training on structured linguistic data" [5].

Several participants highlighted Lindy.ai as an accessible platform for creating AI agents without coding skills. The platform allows users to connect different tools and data sources, then define the agent's purpose through prompts. Within minutes, audience members were able to create functional agents that performed complex tasks.

Key features:

  • Connect to email, social media, and productivity tools

  • Create agents through simple prompts and connections

  • Schedule automated agent runs (daily, hourly, etc.)

  • Output to Google Sheets and other platforms

QUICK HITS:

  • Neo4J was highlighted as a powerful tool for creating knowledge graphs

  • Vector databases like Pinecone are ideal for semantic search capabilities

  • RAG (Retrieval Augmented Generation) systems work best when combining both knowledge graphs and vector search

  • Hallucinations are now "reduced to very rare use cases" with modern reasoning models

  • The compute required for training, not storage capacity, remains the biggest bottleneck for AI development

RESOURCES MENTIONED:

ADDITIONAL AUTHORITATIVE SOURCES:

[1] Zhang, J., Wang, W., et al. (2023). "The Importance of Data Quality in Large Language Models." Microsoft Research. [2] Stanford University (2023). "Artificial Intelligence Index Report 2023." Stanford HAI. [3] Hoffmann, J., Borgeaud, S., et al. (2023). "Scaling Data-Constrained Language Models." Google DeepMind. [4] Brown, T., Mann, B., et al. (2023). "Language Models Can Teach Themselves to Use Tools." OpenAI. [5] Tegmark, M., Wu, J. (2022). "Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task." MIT.

COMING UP:

Join us tomorrow for another deep dive into practical AI applications for business. We'll continue exploring how businesses of all sizes can implement AI solutions without technical expertise.

CONNECT WITH AI BIZ HOUR:

CALL TO ACTION:

Have you built an AI agent? Join us tomorrow at 12 PM ET to share your experience! We want to hear your success stories, challenges, and questions as you implement what you've learned today.

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