What AI Agents Really Are—and How to Use Them

In a recent Synaptiq webinar, Dr. Tim Oates, Co-founder and Chief Data Scientist, helped demystify AI agents by explaining what they are, how they work, and how businesses can use them effectively.

What Is an AI Agent—Really?
Think of an AI agent as a system that senses its environment, thinks about what to do, and then acts—just like a person would. This "sense-think-act" loop can be simple (like a thermostat adjusting temperature) or complex (like a digital assistant that plans a business trip, compares prices, and reschedules based on flight delays).

AI agents are not new. Researchers have been building them since the 1990s. What’s changed is that today's agents are far more capable, thanks to large language models (LLMs) like ChatGPT. These models allow agents to understand language, reason internally, and interact with people in more natural and useful ways.

Agents Are Tools—Not Magic
It’s important to understand what agents can—and cannot—do. Agents today can:

• Operate autonomously (e.g., proactively alert a user to traffic delays)
  
  

• React to changing inputs (e.g., detect a failed software build and rerun it)
  
  

• Collaborate with people (e.g., suggest talking points before a sales call)
  
  

But they’re not perfect. Like human employees, agents can make mistakes. Their behavior depends heavily on the data and instructions they’re given, and they require careful design to be reliable.

Three Agent Design Patterns for Real-World Use

• Reflection: An agent produces a response and then evaluates its own output. This is especially useful in writing, marketing, and legal reviews.
  
  

• Tool Use: Agents recognize when they need help—for instance, performing math or querying an API—and call the right tool for the job.
  
  

• Planning: Agents break down complex tasks (like booking travel or troubleshooting IT issues), delegate subtasks, and adjust based on results.

These patterns can be combined for more powerful workflows, including multi-agent systems that simulate entire departments.

Business Use Cases: Practical Value Without the Hype

So, enough about what they are, how can AI agents be practically used in business today? Here are 2 examples:

• Sales Enablement: Instead of replacing sales reps, an agent can handle tedious CRM updates, prioritize leads, and draft emails—freeing up time for meaningful human outreach. This reduces burnout and improves personalization.
  
  

• IT Troubleshooting: Agents can monitor system logs, suggest likely causes for failure, and even test fixes—helping teams avoid downtime and focus on bigger-picture work.
  
  

In both cases, the value isn’t in eliminating jobs—it’s in making those jobs better.

Interested in learning how AI agents can support your team’s work—not replace it?

Contact Synaptiq at sales@synaptiq.ai to start a conversation about where agentic systems can deliver real value for your organization.

Synaptiq works with companies to build agentic systems that solve targeted business problems. We help identify high-impact pain points, design practical solutions, and ensure that people—not just processes—benefit from intelligent tools. Our team combines deep experience in machine learning and software design with a people-first approach that ensures the tech serves your goals—not the other way around.