Five agents: making agentic AI work for you

AI development is moving past the prototype stage, and agents have shifted from an experimental novelty to an increasingly common part of business infrastructure. Already 62% of organisations are experimenting with AI agents, and momentum is compounding, with predictions that nearly three in four companies will be using agentic AI in their core operations within the next two years. 

No longer just a simple automation opportunity, AI agents are becoming decision-makers and workflow orchestrators, capable of driving revenue and streamlining operational processes. 

AI agent numbers are climbing exponentially, but no tool is the same as the next. Before jumping on the AI agent bandwagon, marketers and the C-Suite need to understand the unique differences and common workflow configurations being used. 

1. Chatbots 

AI chatbots simulate human conversation, designed to process text and audio inputs to generate high volumes of customer inquiries with speed and accuracy. Their presence is solidified in agentic commerce, with 78% of companies are already implementing conversational AI to resolve technical issues and consumer queries. 

In this configuration, the agent checks the task after the initiating request and routes it to the correct tool or service. In a customer support platform, the ‘router’ component will decide whether the agent needs to use a live chat interface, CRM tool, or knowledge base tool to answer the query. 

2. Virtual assistants 

Siri, Alexa, “Okay Google”. These virtual assistants are increasingly using natural language processing (NLP) and artificial emotional intelligence to schedule appointments, set reminders, provide directions, and even make purchases. 

Tools such as Google Assistant utilise external tools to perform tasks with agents receiving the initiating action and sending it to tools, processing results straight into its decision-making process.

3. Recommendation engines 

These systems power platforms everything from Netflix to medical diagnostic tools, using machine learning algorithms to analyse user behaviour and suggest relevant products and services. Reviews, past purchases, and search history all power personalised

While the AI agent will suggest an action, the final result requires human intervention. The human can review, correct or approve decisions made by the AI agent, most common in medical diagnostics and financial monitoring. 

4. Autonomous agents 

Autonomous agents combine machine learning, NLP, and analysis to make real-time decisions based on environmental data. Adoption of physical AI is growing – Deloitte reports 58% of businesses are already using physical AI and this is expected to reach 80% by 2027.

A single AI agent can communicate with other agents as needed, with these secondary agents being chosen to complete a specific task depending on the context or complexity. In self-driving cars, for example, the primary agent can utilise different agents that specialise in navigation or object recognition. 

5. Content and asset generation agents 

AI models now generate articles, scripts, videos, product descriptions, and design assets. These tools are complete with tools to assist in marketing copy, educational resources, social posts – the lot.

Just take a look at the blog header. 

Using the advanced AI generation model, Kling 2.6, our experts used a clear prompt to render the high-fidelity, cinematic asset you see above.

Asking more from AI with IDHL Labs 

AI is evolving at pace and wrapping your mind around its intricacies can feel like a challenge. IDHL Labs was created to bring some clarity to this complexity, staying on top of AI’s evolution and helping our clients enhance their performance in an AI-driven world. If you want to learn more about AI agents and how their capabilities can support your goals, get in touch today.