The term “AI agent” has become a buzzword in the tech world. It appears everywhere from VAPI to ChatGPT assistants. But what exactly is an AI agent? How can businesses use this technology effectively? Let’s dive into the 3 main ways to working with AI agents and explore their real-world applications.
What Makes Something an AI Agent?
Think of an AI agent like James Bond:
you brief them on their mission, equip them with tools, and deploy them into the field. The agent makes autonomous decisions about how to achieve its objectives. You still observe the agent from afar, but don’t interact. In technical terms, an AI agent can control its own workflow. You set the parameters, the tools and the setting. But the AI decides how to navigate within them.
The Three Ways to Work with AI Agents
1. Single Agent (Assistant) Approach
The simplest implementation is a single large language model (like ChatGPT) equipped with specific tools. Such agents are often called “Assistant”. These tools might include:
- Weather lookup capabilities
- Web search functionality
- Image generation abilities
Real-World Example: OpenAI’s ChatGPT interface is a prime example of a single agent system. Another successful implementation is DentroChat. It demonstrates how a well-designed single agent can help with daily tasks. We use it all the time for research, brainstorming, checking facts, helping us with blog posts… 😉
2. Sequential Agents
This approach chains multiple agents together, each with its own:
- Unique system prompt
- Specific set of tools
- Potentially different underlying AI models
Real-World Example: Consider the blog content pipeline used to write this very post. Different agents handle specific tasks:
- Agent 1: Transcribes spoken content
- Agent 2: Sources reference materials
- Agent 3: Adds internal website links
- Agent 4: Generates images
- Agent 5: Formats HTML according to guidelines
3. Supervisor-Based Teams
This structure involves a main supervisor agent orchestrating multiple sub-agents:
- The supervisor analyzes incoming requests
- It delegates tasks to specialized sub-agents
- Multiple agents can work together on complex problems
Real-World Example: For one of our clients we used the supervisor team. Their customer support chat has one supervisor agent. This supervisor routes queries to specialized agents. The subagents then handle technical issues, complaints, job applications or other specific areas (AI in customer service)
Implementation Challenges and Best Practices
Key Considerations:
- More control generally leads to better performance for specific use cases
- Sequential agents offer precise control but require more setup and edge case planning
- Flat frameworks (single agents) handle edge cases independently but may need stronger system prompts
Getting Started
Ready to build your first AI agent? Here’s how to begin with single agents:
- Sign up for a free account at either:
- Experiment with building basic assistants
- Start with a single agent approach before moving to more complex implementations
Want to build more advanced sequential agents or supervisor teams?
- Try out the no-code tool Flowise.
- The screenshots above are taken from our Flowise Flows ✅
- Here I explain in 180 seconds how to self-host Flowise
- Use highly flexible python frameworks such as Langgraph or CrewAI.
The Bottom Line
The key to using AI agents lies in choosing the right approach for your specific needs. Whether you opt for a single assistant, sequential agents, or a supervisor-based system. Understanding these 3 fundamental approaches will let you decide about your AI strategy.
Remember: The best approach balances control and autonomy. Too much control requires more work but ensures precision. More autonomy can handle unexpected situations but may require stronger initial guidance.
Further Reading
- ChatGPT Is a Tipping Point for AI – Harvard Business Review
- Why agents are the next frontier of generative AI – McKinsey & Company
- What are AI agents? – MIT Technology Review