How to Build AI Agents

From Code to Capability: A Practical Demo of Agentic AI in Action

In a recent Synaptiq webinar, Dr. Tim Oates—Co-founder and Chief Data Scientist—walked viewers through a live demonstration of building a fully functional agentic system using OpenAI's Agent Builder. The session showcased just how far low-code/no-code tools have come in enabling real AI integration without needing to write traditional software. Below is a recap of that session, offering insights into what agentic systems are, how they work, and how leaders can start using them today.

Agent vs. Program: What's the Difference?

Rather than just presenting data or predictions, agentic systems perform actions: they retrieve web results, make decisions, summarize insights, or route information—all on your behalf.

Before jumping into the demo, Tim explained a foundational shift:

• Traditional programs run step-by-step logic. You tell it exactly what to do, and it does it.
  
  
• Agents are more autonomous. You give them a goal (e.g., "Find research grants for this project idea") and they figure out the steps, pulling from tools, data, and Large Language Models (LLMs).

This difference is crucial when thinking about how AI gets embedded in real-world business workflows.

Demo Overview: Building an Agentic System Live

Tim used OpenAI’s Agent Builder to construct a full workflow, live on the webinar. The use case? An AI system that helps a user submit a research proposal by identifying matching federal grant opportunities. This could easily be adapted to business cases like identifying sales leads, investment opportunities, or customer feedback.

Step 1: Input Validation

The system begins with a user input (a proposed idea for funding). The first agent checks two things:

• Is the input between 20–50 words?
  
  
• Is it a valid project idea suitable for federal funding?

This is powered by an LLM and outputs a structured JSON response. If the input fails, the system gracefully exits.

Step 2: Query Generation:

When validated, the input goes to a Query Generator Agent that constructs 4–6 relevant web search queries. These are tailored to federal agencies (e.g., NSF, NIH, DOE) and aim to locate matching Requests for Proposals (RFPs).

The output is again structured JSON, including:

• Query string
  
  
• Priority ranking
  
  
• Target domain

Step 3: Web  Search and Extraction

Those queries are passed to a Search and Extract Agent, which:

• Runs the web searches
  
  
• Evaluates the results
  
  
• Returns the top 4–6 matching opportunities

Each opportunity includes:

• A URL
  
  
• Relevance score (1–5)
  
  
• Proposal due date
  
  
• A custom summary of how the project could align with the opportunity

Tim also turned on "Tool Use" within Agent Builder, allowing the AI to access web search functionality directly.

Why This Matters

This system—built live, in under an hour—highlights several key takeaways:

• LLMs are capable of sophisticated logic and judgment. The validation agent could distinguish a valid funding idea from a nursery rhyme.
  
  
• Structured outputs (JSON) matter. They make the handoff between agents possible and reliable.
  
  
• No-code/low-code tools lower the barrier to AI adoption. Business users, researchers, and product managers can prototype powerful workflows without writing a line of code.
  
  
• Real-world problems drive meaningful design. By grounding the system in a task that Tim personally faces—finding funding—the agent performs highly practical work
  

From Demo to Deployment: Considerations

While Agent Builder is great for prototyping, production deployment still has hurdles:

• Managing cost and latency (you may not need GPT-4 for every step)
  
  
• Ensuring robustness across edge cases
  
  
• Creating clear permissioning, guardrails, and QA for real users
  
  

However, Agent Builder does export to Python, and for many small-scale use cases, the scaffolded workflows are already useful.

Tim also mentioned tools like Dify, a popular open-source alternative that supports more control and customization.

Final Thoughts 

Agentic AI isn't a buzzword—it's a shift in how work gets done.

By combining clear instructions, structured outputs, and the autonomy of large language models, agentic workflows bring AI one step closer to acting as a true collaborator—not just a tool.

The next generation of digital systems won’t just compute. They’ll decide. They’ll search. They’ll summarize. They’ll act.

And thanks to platforms like Agent Builder, that future is already here.

What Synaptiq Does—and Why Agentic Systems Matter

Synaptiq has evolved over the past decade from delivering custom machine learning solutions to helping organizations design and deploy full intelligent products. These go far beyond model outputs, integrating AI with product design, UX, strategy, and decision workflows. Agentic systems—where AI acts on its own to accomplish goals—are a natural next step in this evolution.

Want to start building your own AI agents?
Synaptiq can help you identify the best use cases, design behavior-driven workflows, and turn smart prototypes into scalable systems.

Reach out here to learn more or discuss your next project.