Agent Demonstration
1. Agent Demonstration
Up until now, we've talked about concepts and use cases, but there's nothing quite like watching an autonomous agent work in real-time. You'll see its plan, its approach, execute across multiple data sources, synthesize insights that would take a human analyst hours to compile. In the next few minutes, you're going to witness exactly what autonomous intelligence looks like in action. We explore the concepts behind agents and seeing how they transform sales and customer service. This time to see autonomous intelligence in action. We're going to revisit the sales intelligence agent that has access to the same type of data you'll be using with this course. For this demonstration, we have an agent with access to structured data, sales metrics, deal values, close dates, win and loss status, and unstructured data, sales conversation transcripts, customer meeting notes, and discovery call details. First, let me show you the difference between directed and autonomous approaches with the same business question. Here's a direct approach. I'll ask, show me sales metrics for 2024 for enterprise customers. The system shows us the sales metrics for all 2024 enterprise customers. It found two deals, two new customers with a total revenue of $170,000. The interaction ends there. Now watch the autonomous approach. I'll ask, help me understand our sales performance and identify which deals we should prioritize for closing. Watch what happens. This is a completely autonomous. The agent is now planning its approach. You can see it's decomposing this objective into specific sub-tasks. First, it's going to get the overall sales performance metrics. Second, identify pending and open deals that need attention. Third, analyze deal characteristics to suggest prioritization criteria. Fourth, use Cortex search to see if there's any conversations insights about specific deals. And finally, generate appropriate visualizations. Notice that I didn't specify these steps. The agent determined this plan autonomously based on the business objective. Watch as the agent executes each part of its plan. Step one, we can see the deal pipeline. We can see which ones already been closed and which ones are coming up. Then we see it executing the query itself, getting in that data. Then we are looking at any future dates for any closing or pending deals in the future. And it found some. So we have a couple of deals that are closing within 2026 that will help us understand our full pipeline. And from there, we're getting some sales conversations insights, so that way we can incorporate those together at the end. Watch how the agent reflects on its findings and synthesizes insights. The agent is now combining all these data points and reasoning about what they mean together. This is where real intelligence happens, connecting patterns across different data sources that humans might miss. Look at what the agent autonomously discovered and synthesized. We see here that our tiniest priority is health tech solutions for $120,000. And we have Rachel Torres on that. And the key insights we found is extended technical sessions with their IT security team is important. We also need to focus on the HIPAA compliance elements as well. Next is growth startup for 100,000 with Sarah Johnson. They have 500 employees, which scalability is a problem. The major pinpoints they have is system crashes and limited reporting and poor remote team support. So they're very interested in the product itself. Next, we have some strategic recommendations as well. So we can have follow-up actions with our three highest priority deals that we have, and as well as any resource allocation that we need for our team. And we have more pipeline insights. Compare this to the directed approach. Directed gives you simple queries, simple answers. People have to ask follow-up questions. Autonomous gives you complex objective, comprehensive analysis, and actionable recommendations. The agents didn't just answer a question. It solved a business problem. How? By discovering insights I didn't specifically ask for, connecting patterns across multiple data sources, providing specific actionable recommendations, identifying both opportunities and risk. This demonstration shows several key autonomous capabilities. First, cross-model integration. The agents seamlessly combined structured deal data with unstructured conversation insights. Second, contextual reasoning. It understood that technical concerns need proactive addressing. Third, proactive intelligence. It identified deal priorities and obstacles that weren't explicitly requested. Fourth, business context. The recommendations are specific and actionable, focusing on concrete next steps. What you just witnessed is the future of business intelligence. These are systems that don't just respond to questions, but work autonomously towards your business objectives. Instead of building applications that wait for directions, we're building systems that understand goals and works towards them independently. This is the power of agentic AI, and it's what you'll be building throughout the rest of this course.2. Let's practice!
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