What is an AI Agent?

Introduction

The global conversational AI market is projected to grow exponentially, from $15.5 billion in 2024 to $132.86 billion by 2034, with a CAGR of 23.97%. This rapid growth highlights how conversational AI is reshaping the way businesses engage with customers and stakeholders across their user journeys. Autodm AI empowers businesses with complex purchasing journeys that traditionally require a human intervention — such as in travel, real estate, automotive, and energy efficiency — to capture and convert in the order of 300% more sales opportunities from inbound traffic. By leveraging a multi AI Agent approach, Autodm AI personalises customer interactions and maximises lead conversions right at the source.

This article is crafted for business professionals with minimal technical expertise. Here’s what you’ll learn:

• What conversational AI agents are and how they work
• The difference between single AI vs. multi-AI systems and their impact
• Key considerations when selecting a vendor
• The business benefits of conversational AI

Let’s dive in.

What Are AI Agents?

‍Traditional AI software applications leverage machine learning models and LLMs (Large Language Models) to help with narrow and well-defined tasks — responding to customer queries, analysing data, or drafting reports — but leave a significant burden on you to decide on the next step and verify each output's quality. This often results in inefficiencies and wasted time.

AI agents take it a step further. They are autonomous systems capable of reasoning, decision-making, and executing tasks independently. Think of them as virtual team members who don’t just assist but proactively own tasks, working creatively within defined boundaries.

For businesses with complex purchasing journeys, AI agents must go beyond basic query resolution. They need to:

• Ask clarifying questions to uncover customer needs.
• Provide personalised recommendations.
• Capture customer intent and seamlessly convert prospects into customers.
• Answer all customer questions
• Route customer to the optimal sales conversation channel

Why Do Traditional Chatbots Fall Short?

Chatbots gained popularity in the 2010s, fueled by advancements in AI, machine learning, and natural language processing (NLP). Yet, let’s be honest—despite all the promises of 24/7 support, most chatbots have fallen short, often leaving users frustrated and unheard.

A recent study paints a stark picture:

• 78% of customers used a chatbot last year, but 80% found the experience frustrating.
• 63% faced unresolved issues, and 72% felt the experience was a waste of time.
• Over 54% of users still believe live phone support offers a faster, better experience.

Poor customer experiences drive potential leads away and leave businesses struggling to keep up in competitive markets, and this is amplified in industries where products are particularly complex. 

Now, picture this instead: your customers engage with a conversational AI agent — one that doesn’t just respond but actively solves problems. Imagine an agent that:

• Asks logical, personalised questions to truly understand your customer’s needs.
• Provides instant, real-time answers and recommendations that are clear and actionable.
• Organises requests seamlessly and passes them on to human agents when necessary.

At Autodm AI, we’re making this come true. Businesses leveraging our conversational AI agents have already experienced a 300% increase in lead capture and a 50% boost in sales conversions.

Conversational AI agents don’t just replace chatbots — they elevate the entire customer experience, and aim to actively move users further along the sales funnel and potentially all the way to the sale, instead of simply answering basic questions.

Single AI Agent vs Multi AI Agent: What's the Difference?

To dive deeper into the technicality of conversational AI, there are two main paths you can take depending on your use case: you can opt for either a single AI agent, or take a multi-agent approach.

Single AI suits very simple tasks, while Multi AI excels in complex, high-value user journeys requiring collaboration and nuanced insights.

A single AI agent struggles with complex tasks, while Multi AI works like a team of experts, streamlining user needs and either passing organised insights to a human to make the sale, or directly leading the prospect to an online e-commerce purchase. Autodm AI’s multi-agent model has helped companies boost their user insights captured by 1000%, driving 10x more leads and improving marketing strategies.

It’s important to note that the best approach may vary as user needs and technologies evolve over time. Meanwhile, here’s a breakdown of the key differences.

How AI Agents Work: A Real-World Example 

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Imagine Sarah, a prospect of your travel business, visits your website and requests: “I’m looking for a winter trip to India within my $2,000 budget”

Behind the scenes, Autodm AI’s multi-agent system springs into action:

1. Understanding the Query: The AI Orchestrator, (a tool that helps different AI systems work together smoothly to get things done) breaks Sarah’s request into smaller tasks and assigns them to specialised AI agents.
2. Agent Collaboration:
   • Agent 1 queries internal travel databases and APIs for matching itineraries of the company.
   • Agent 2 generates clarifying questions that are relevant to understand her preferences (is Sarah looking for an organised tour, a hotel stay, or other?) 
   • Agent 3 gathers company-specific information on its value proposition
3. Seamless Delivery: The orchestrator synthesises the data into a message:
   • Incredible choice, India is fantastic over the winter - Sarah is going to love it. She could enjoy a luxury stay in Rajasthan, a boutique experience on the Kashmir, or even a yacht trip through the Andaman & Nicobar Islands
   • Can Sarah tell us more about what she’s looking for? Is she dreaming of relaxing on beautiful landscapes, exploring historic towns, enjoying luxury stays, or a mix of everything? 
   • We’ll pull together some amazing options for Sarah to make this a summer trip she’ll never forget. The company is specialised in crafting highly tailored and personalised trips
4. Iterative Guidance: this back-and-forth between Sarah and the AI continues until she has all the info needed to make her trip purchase.

Result: Sarah receives her ideal vacation options in seconds, leading to faster conversions and increased customer satisfaction. Sarah feels supported every step of the way, which not only helps her feel less overwhelmed but also empowers her to confidently project herself into the experience she’s about to choose. Sarah can either make the purchase online on the company’s website, or be passed on to the travel agent, who now knows Sarah’s preferences, whether she’s okay with the options, and can directly call her to further tailor the trip to her liking.

Best Practices for AI Agent Development

Choosing and deploying the right AI agent is key to ensuring that you’ll see the right results. To ensure success, here are the key factors to keep in mind:

1. Flexibility to Add Your In-House AI
   • The vendor’s system should allow seamless integration of your own AI tools. This ensures your in-house team can collaborate effectively with the vendor to meet your unique requirements.
2. Align the AI Agent with Your Business Goals‍
   • The AI agent must serve your specific needs and goals, and is specialised for your industry. For example, in real-estate, your multi-AI agent systems should at least include an AI agent who can proactively ask the right follow-up questions.
   • This should help the AI to provide detailed, resourceful responses, while pushing for a sale by offering bundled offers or personalised recommendations based on the area, budget, financing options and availability within seconds. It must be trained to run the conversation with the goal to capture the user's intent, with the right knowledge of the industry.

3. Ensure Enterprise-Grade Safety and Compliance
   • Safeguards are essential, especially if your AI will handle customer interactions at scale. Your multi-AI agent systems should at least include an AI agent who can:
     • Protect private customer data (PII).
     • Moderate unethical or harmful topics.
     • Include defenses against hacking or malicious attacks.
     • Comply with legal standards like GDPR and CCPA to ensure ethical and legal use.
   • It’s important to discuss privacy, security and compliance measures with your vendor early on to ensure you’re not getting yourself into legal troubles later on. 
4. Vendor’s Testing and Performance Monitoring
   • Choose a vendor with strong systems to test, monitor, and improve AI performance. This includes:
     • Evaluating how well the AI answers questions over time.
     • Ensuring the orchestration of multiple AI agents is accurate.
     • Regular updates to improve quality and functionality.
5. Strong Technological Foundation
   • AI agents are just one piece of a larger system. A good solution requires:
     • A robust data system to feed the AI.
     • Scalable architecture to adapt to future needs.
   • Invest in an AI platform not a black box software with specific ways to interact with a given LLM. AI research is moving extremely fast, with better models released every week and older models becoming obsolete in ~6 months. A platform can help you bring your use cases to market in weeks and constantly adapt to best-in-class models.

At Autodm AI, we cover it all - we design multi-agent systems that fit your industry, helping you capture user intent and achieve real results. We focus on safety, easy integration with your tools, reliable performance and monitoring, and a flexible setup that grows with your needs.

Benefits of AI Agents For Sales and Customer Experience:

1. Driving Lead Generation and Conversions
   Conversational AI agents streamline the customer acquisition process by capturing their intents at the right time and the right channel (e.g., website, Whatsapp), personalizing product recommendations, and converting prospects effectively. For example, Autodm AI’s conversational AI has helped clients achieve a 300% increase in leads generated and a 50% boost in sales conversions.
2. Increasing Revenue Potential
   Intelligent lead qualification and scoring capabilities enable AI agents to recommend relevant products or bundles, providing optimal proposals and routings. By capturing 1000% more consumer data, businesses can refine offers and maximise revenue opportunities, translating into greater profitability.
3. Enhancing Productivity and Efficiency
   By automating repetitive tasks like answering FAQs or data entry, AI agents free up human teams to focus on strategic initiatives. This shift significantly enhances overall productivity and reduces operational bottlenecks, improvement in productivity through AI adoption.
4. Elevating Customer Experience and Insights
   AI agents provide fast, personalised, and consistent service 24/7, ensuring customers feel valued and supported anytime. Additionally, they capture deeper insights into customer behaviours and preferences, empowering businesses to make smarter, data-driven decisions. 

Turn Every Customer Interaction into a Lead

Businesses with complex and long purchasing journeys and high volume contracts thrive on conversations and connections — but scaling them is a global challenge today. What if you could convert a majority of your website visits into actionable leads? What if every inquiry was handled with precision, 24/7, while seamlessly driving customers toward a purchase?

Autodm AI makes this possible. Our multi-agent conversational AI doesn’t just handle interactions; it turns them into measurable sales results, with smarter, faster responses tailored to your business. 

If this sounds interesting for your business, book a call with us here.