Yesterday we talked about what AI agents are and their potential impact on your business. Today, let’s peek under the hood to understand how they actually work.

If you’ve ever stayed at a high-end hotel, you’ll be familiar with their concierge service: those incredibly helpful professionals who seem to know everything and can get anything done. As it turns out, AI agents work in surprisingly similar ways.

Think about what happens when you approach a hotel concierge with a request. You might ask them to find you a great local restaurant, make reservations, and arrange transportation.

Seem simple? There’s actually quite a bit happening behind that smooth experience. Just like a hotel concierge, AI agents have three key components that work together to help you get things done.

First, there’s the knowledge base (what we call the model in AI terms). Just as a great concierge knows their city inside and out, the AI model contains broad knowledge about its domain. But there’s a key difference: while a concierge actively learns about new restaurants and events, an AI model’s basic knowledge stays fixed unless it’s specifically updated with new information. That’s why the next component is so important.

The second component is the set of tools the agent can use. Your hotel concierge doesn’t just rely on their memory – they use reservation systems, booking platforms, local contact lists, and review sites to get things done.

Similarly, AI agents use various tools to access real-time information and take actions. When you ask an AI agent about flight prices, it’s not pulling from outdated information – it’s using tools to check current rates, just like a concierge would call their travel partners.

The third component is what really brings it all together – orchestration. This is like the concierge’s ability to handle your request from start to finish. When you ask for dinner recommendations, a good concierge doesn’t just list restaurants. They consider the time, your preferences, availability, location, and transportation options. They might check reviews, make a reservation, book a taxi, and provide you with directions – all in the right order.

AI agents work the same way. When you give them a task, they use their orchestration layer to decide what steps to take and which tools to use when. If you ask an AI agent to help plan a product launch, it might first check your calendar tool for available dates, then use a market research tool to gather competitor information, and finally access your email system to draft announcement messages.

Let’s say you ask an AI agent to analyze customer feedback. Like a concierge planning your perfect evening, the agent:

• Uses its model to understand what constitutes meaningful customer feedback
• Employs tools to gather data from various sources like surveys and social media
• Orchestrates the process by deciding which data to gather first, how to analyze it, and how to present the findings

Understanding these components helps you make better use of AI agents.

Just as you wouldn’t ask a hotel concierge for generic advice but rather specific, actionable help, you’ll get better results from AI agents when you give them clear, specific tasks that make use of their tools and orchestration capabilities.

One key difference from our concierge analogy? AI agents can handle multiple complex tasks simultaneously without getting tired or overwhelmed. They can monitor various data sources, analyze trends, and generate reports all at once – though always within the boundaries of their configured tools and capabilities.

When you’re thinking about using AI agents in your work, consider how these components might come together in your specific field.

A social media manager might need an agent that combines market trend knowledge (model) with social listening tools and posting capabilities (tools), all coordinated to maintain consistent brand voice and timing (orchestration).

A financial advisor could use an agent that understands investment principles (model), connects to market data feeds and portfolio management systems (tools), and coordinates analysis and reporting tasks (orchestration).

In healthcare administration, an agent could combine medical billing knowledge (model) with insurance verification systems and scheduling platforms (tools), orchestrating patient follow-ups and insurance claim processing.

For HR professionals, an agent might pair recruitment best practices (model) with applicant tracking systems and job board integrations (tools), coordinating candidate communications and interview scheduling (orchestration).

Legal assistants could benefit from agents that understand legal terminology and procedures (model), work with document management systems and court filing tools (tools), and coordinate deadline tracking and document preparation (orchestration).

Meanwhile, real estate agents might use agents that combine property market knowledge (model) with listing databases and client management systems (tools), orchestrating property matches and viewing schedules.

Event planners could leverage agents that understand event management principles (model), connect with venue booking systems and vendor databases (tools), and coordinate timelines and logistics (orchestration).

For content creators, an agent might combine industry knowledge (model) with content management systems and analytics tools (tools), orchestrating content calendars and performance tracking.

E-commerce managers could use agents that understand retail trends (model), integrate with inventory systems and pricing tools (tools), and coordinate product launches and promotional campaigns (orchestration).

Research analysts might benefit from agents that combine analytical frameworks (model) with data gathering tools and visualization platforms (tools), orchestrating report generation and insight delivery.

IT support specialists could use agents that understand technical troubleshooting (model), connect with system monitoring tools and ticket management systems (tools), and coordinate incident response and resolution workflows (orchestration).

What makes this really interesting is how these components adapt to different scenarios. Sometimes you need an agent that’s heavy on real-time tools, like when monitoring customer service channels. Other times, you might need one that relies more on its base knowledge, like when drafting initial responses to common questions. The orchestration layer adjusts accordingly, like our concierge knowing when to make quick decisions versus when to gather more information first.

The real skill lies in recognizing which combination works best for your specific needs. Just as you wouldn’t ask a concierge to redesign your hotel room, you’ll get the best results when you align an agent’s capabilities with tasks that match its strengths.

Next Steps

Now that you understand how AI agents work, you can be more strategic in using them. Start by examining one specific task in your workflow.

Ask yourself:

• What knowledge does this task require? This helps you understand if an AI agent has the right base model for the job. For a customer service task, check if the agent understands your product terminology and common support scenarios.
• Which real-time information or actions are needed? This reveals the tools your agent should access. In sales, you might need connections to your CRM, pricing systems, and product inventory.
• How do different parts of the task connect? This shows the type of orchestration required. For content creation, consider how research, writing, and publishing need to flow together.

Pick a small, well-defined task to experiment with first. Watch how the agent uses its knowledge, tools, and orchestration. This observation helps you refine your approach and build confidence in working with AI agents effectively.