Examples: most enterprise AI agents and project management chatbots
practical cases for companies
Businesses benefit from LLM agents in areas that involve processing and responding to natural language, such as answering questions, providing guidance, automating workflows, and analyzing text.
Companies often use LLM agents for marketing, data analysis, regulatory compliance, legal assistance, healthcare support, financial tasks, and education.
Here are 3 of the most popular use cases for llm agents:
customer service
llm are widely used in customer service to handle frequently asked questions, troubleshoot issues, and provide 24/7 support.
These agents can interact with customers in united kingdom phone numbers real time, offering immediate help or referring complex queries to human agents.
See also:
Sales and lead generation
In sales, LLM agents qualify potential customers by engaging in conversations with them, assessing their needs, and gathering valuable information.
They can also automate follow-up interactions, sending personalized recommendations or product information based on the customer's interests.
See also: how to use AI for sales
internal support: hr and it
For internal support, LLM agents streamline HR processes. And you managing the usual employee queries. In HR, they answer questions about topics like benefits, leave policies, and payroll, while in IT, they solve basic technical problems or automate routine tasks like account setup.
This allows HR and IT teams to focus on more complex responsibilities, rather than repetitive work.
See also: the best AI agents for HR
how to create an llm agent
define objectives
Clarify what you want the llm agent to accomplish, whether it's helping with customer inquiries, generating content, or managing specific tasks.
The definition of clear objectives will determine the installation and configuration of the agent.
Choose a platform from ia
The best AI platforms will depend entirely on your goals and needs.
Select a platform that fits your needs, considering factors such as customization options, integration capabilities, ease of use, and support.
The platform should:
support the desired use case
offer your calls
offer integration capabilities
configure the call
Based on platform options, choose a pre-built LLM or fine-tune a model for specialized tasks if necessary.
Many platforms offer built-in, pre-formed, and ready-to-use language models.
If you're interested in customizing your use of llm, read our article on how to choose a custom llm option for your ai project from our growth engineer, patrick hamelin.
Integrate tools
Most platforms offer integration options for external tools. Connect any API, database, or resource your agent needs to access, such as CRM data or real-time information.
Test and refine
Test the agent thoroughly using the platform's built-in testing tools. Adjust parameters, question wording, and workflows based on test results to ensure that the agent performs well in real-world scenarios.
Deployment and control
Use the platform's monitoring tools to track agent interactions and performance after deployment.
