What is “Agentic AI” and Why Adopting a Multi-Agent Approach is Essential

Leaders barely had the time to digest what "LLMs" and "AI" could mean for their businesses before other terms emerged like "AI Agents" or "Agentic AI".

But what are these new approaches? How is it different from LLMs and RAG? Why does it matter to you? And how can they help your business succeed?

We see their importance daily at Autodm AI : AI Agents and Agentic AI are not just the next cool thing.

AI Agents, and multi-agent orchestration, are the key to the mass adoption of AI by businesses and their customers?

Core Insight

Despite the remarkable advancements in language models (LLMs) over the past decade, these models alone cannot solve the needs of businesses:

• Sustain engaging conversations with their users
• Reliably answer customers' questions within a business context
• Proactively ask questions to refine the user needs and recommend the best products
• Qualify and orient customers to the right channel for a cost-effective treatment

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Direct Use of LLMs and Hallucinations

Relying solely on models from OpenAI, Anthropic, Google, etc., is ineffective. To answer questions from the user, these models have no choice but to depend on their (impressive) internal memory (i.e., the training data) and cannot access enterprise data, product catalogs, internal content, etc.

This leads to bugs called hallucinations, where the models generate responses that are factually incorrect - and worse - without the end user even realizing it!

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Why Retriever Augmented Generation (RAG) is not Enough

A more advanced use of LLMs involves providing context - information the model can synthesize to answer user questions. If there is too much information, a semantic search (as opposed to keywords-based search) in a specialized database ("vector store") can select relevant content to feed the LLM.

While useful if implemented correctly (it helps contextualize the question and tamper hallucinations), the RAG approach can only work for a subset of the problem: information retrieval.

This limited approach, used by the vast majority of AI software providers today, also poses challenges in updating the vector base with highly dynamic content (product catalog, promotions, inventory, etc.), thus falling short of providing a good customer experience.

AI Agents beyond RAG

It is impossible for businesses to address all their customers needs with a single AI, they need many (smaller) specialized AI called AI Agents.

These agents are designed for specific tasks, such as:

• Collect relevant information from text, images, etc. or more structured databases
• Answer FAQ and Moderate conversations
• Extract key information from conversations and Ask users clarifying questions
• Make requests to third-party APIs (search, weather, government sites, calculators, etc.)
• Send information to third-party systems (CRM, calendar, quote generation, etc.)

In that new taxonomy, RAG is reduced to a technique used by an Information Retrieval Agent.

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Multi-Agent Approach ("Agentic AI")

Finally, we need to coordinate the work of multiple AI agents to achieve business objectives. This is the job of the orchestrator.

We need a manager for those AI Agents (we call it an "orchestrator"), who can:

1. Capture the needs and intents of the customer, as they evolve during the conversation
2. Understand the strengths of each agent and which ones to call
3. Assign those agents some work (information retrieval, next qualifying question to ask, best products to recommend, etc.)
4. Combine their contributions into a rich message to best interact with the customer.

Doing so Agentic AI can fully engage with customers, answer a wide variety of questions, identify which information to collect and which product to recommend, and ultimately convert visiting prospects into satisfied customers.

Multi-agent orchestration is therefore essential to deliver the conversational quality necessary for helpful and engaging conversations with customers, to achieve the business objective and deliver economic value.

At Autodm AI , we successfully designed and operate multi-agent orchestration to boost customer engagement and increase sales. If you are interested please get in touch.