As artificial intelligence (AI) reshapes industries, AI agents are emerging as pivotal tools to enhance automation, simplify workflows, and deliver strategic insights.
But what exactly are AI agents, and how can businesses leverage them to their advantage?
Let’s delve into practical use cases, workflows, and the integral role of Large Language Models (LLMs) in driving agentic systems.
Business use cases for AI agents
AI agents address diverse challenges and create opportunities across industries. Here are some compelling examples:
Data agents These agents answer natural language questions and generate reports instantly by accessing SQL, NoSQL databases, or file-based data sources, making data insights more accessible.
Search agents Search agents seamlessly sift through multiple information sources to provide relevant results. From real estate and travel to retail or event discovery, these agents simplify complex searches.
Customer Support agents Enhancing customer experience, these agents integrate information from multiple documentation sources to provide accurate and dynamic responses using natural language.
Content generation agents Leveraging internal company data, these agents generate tailored content like reports or structured documents, boosting productivity and creativity.
Survey agents Dynamic and adaptive survey agents offer interactive experiences that outshine static survey systems, delivering personalised and engaging customer interactions.Business use cases for AI agents
When to use AI agents
AI agents excel in dynamic tasks where workflows are unpredictable or undefined. They:
- Act as intermediaries between users and systems that are complex or inaccessible.
- Automate intelligent tasks, adapting to context in real time.
- Support simulations with multi-agent systems for collaborative problem-solving or scenario modelling.
For example, an agent in a simulation might act as a negotiator, data provider, or strategist, collaborating within a team of agents to achieve shared objectives.
When static workflows are sufficient
Static workflows are better suited for well-defined processes. Unlike agents, these workflows have a fixed sequence of steps, making them efficient for predictable tasks.
An example is a Retrieval Augmented Generation (RAG) system for customer support:
- A user asks a question.
- Relevant documents are retrieved from a database.
- The question and documents are processed by an LLM.
- The LLM provides a response to the user.
While agents thrive in adaptability, static workflows offer stability and simplicity for routine processes.
The role of LLMs in agentic workflows
LLMs form the decision-making core of agentic workflows by:
- Identifying which tools to use based on task requirements.
- Orchestrating processes dynamically, informed by the agent’s interaction history.
- Generating comprehensive responses for end users.
These models allow agents to access real-time or domain-specific information beyond their training data, enabling capabilities like forecasting, accessing internal enterprise databases, or retrieving the latest news.
Swarms in AI agents
AI swarms are decentralised systems of multiple agents working collaboratively to solve complex challenges. Inspired by swarm intelligence in nature, these agents communicate, adapt, and make collective decisions, enabling dynamic problem-solving and emergent behaviours. Swarms are highly scalable, resilient, and efficient, making them ideal for applications like supply chain optimisation, financial risk management, healthcare coordination, and smart cities. By leveraging shared goals and real-time feedback, swarms offer innovative solutions to multifaceted tasks, though they require robust human oversight, coordination, and infrastructure to manage their complexity effectively.
The future of AI agents
The potential of AI agents is boundless. Emerging innovations, such as agent swarms and enterprise-level agent infrastructures, will further revolutionise automation. These systems will empower businesses to tackle increasingly complex challenges with greater agility.
By integrating AI agents and understanding when to use static workflows or dynamic agents, businesses can unlock new efficiencies and drive transformative outcomes. From improving customer service to fostering innovation, AI agents are shaping a smarter, more responsive future.
In our upcoming Onepoint TechTalk: Decoding AI Series webinar, our AI Solutions Engineer, Gil Fernandez, will dive into this topic in greater depth. Join us live or visit us for the replay at your convenience.
AI agents are transforming how businesses operate, offering unparalleled opportunities for innovation and efficiency. By understanding their building blocks — anatomy, error handling, memory types, and applications — you can harness their potential to create solutions that drive value and impact.