Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
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AI agents powered by large language models (LLMs) help organizations streamline and reduce manual workloads. These agents use multilevel, iterative reasoning to analyze problems, devise solutions, and execute tasks with various tools. Unlike traditional chatbots, LLM-powered agents automate complex tasks by effectively understanding and processing information. To avoid potential risks in specific applications, maintaining human oversight remains essential when working with autonomous AI agents.
In this post, you’ll learn how to build a human-in-the-loop AI agent using NVIDIA NIM microservices, an accelerated API optimized for AI inference. The post features a social media use case to showcase how these versatile AI agents can handle complex tasks with ease. With NIM microservices, you can seamlessly integrate advanced LLMs into your workflows, providing the scalability and flexibility required for AI-driven tasks. Whether you‘re creating promotional content or automating complex workflows, this tutorial is designed to accelerate your processes.
To see a demo, watch How to Build a Simple AI Agent in 5 Minutes with NVIDIA NIM.
One of the biggest challenges marketers face today is generating high-quality, creative promotional content across platforms. The goal is to create varied promotional messages and artwork that can be published on social media. 
Traditionally, a project leader assigns these tasks to specialists like content writers and digital artists. But what if AI agents could help make this process more efficient?
This use case involves two AI agents—the Content Creator Agent and the Digital Artist Agent. These AI agents will generate promotional content and submit it to a human decision-maker for final approval, ensuring that human control remains central to the creative process. 
Building this human-in-the-loop system involves creating a cognitive workflow where AI agents assist in specific tasks, while humans perform the final decision-making. Figure 1 outlines the interaction between the human decision-maker and the agents.
The Content Creator Agent uses the Llama 3.1 405B model, accelerated by NVIDIA LLM NIM microservices. LangChain ChatNVIDIA with NIM functional calling and structured output are also integrated to ensure organized, reliable results. ChatNVIDIA is an-open-source Python library contributed by NVIDIA to LangChain that enables developers to easily connect with NVIDIA NIM. These combined capabilities are consolidated into LangChain runnable chain (LCEL) expressions, creating a robust agent workflow.
Begin by constructing the Content Creator Agent. This agent generates promotional messages following specific formatting guidelines, using the NVIDIA API catalog preview API endpoints. NVIDIA AI Enterprise customers can also download and run NIM endpoints locally. 
Use the Python code below to get started:
Next, we introduce the Digital Artist Agent, which transforms promotional text into creative visuals using the NVIDIA sdXL-turbo text-to-image model. This agent rewrites input queries and generates high-quality images designed for social media promotion campaigns. The following code provides an example of how the agent integrates:
Use the following Python script to rewrite user input queries into image generation prompts:
Next, bind the image generation into the selected LLM and wrap it in LCEL to create the Digital Artist Agent:
To maintain human oversight, the agents will share their outputs for final approval. A human decision-maker will review both the text generated by the Content Creator Agent and the artwork produced by the Digital Artist Agent.
This interaction allows for multiple iterations, ensuring that both the promotional messages and images are polished and ready for deployment.  
The agentic logic places humans at the center as decision-makers, assigning the appropriate agents for each task. LangGraph is used to orchestrate the agentic cognitive architecture.
This involves a function that asks for human input:
Next, create two additional Python functions to serve as graph nodes, which LangGraph uses to represent steps or actions within a workflow. These nodes enable the agent to execute specific tasks sequentially or in parallel, creating a flexible and structured process: 
Finally, bring everything together by connecting the nodes and edges to form the human-in-the-loop multi-agent workflow. Once the graph is compiled, you’re ready to proceed:
Now, launch the app. It prompts you to assign one of the available agents for the given task.
First, query the Content Creator Agent to write promotion text, including a title, message, and social media hashtags (Figure 2). Repeat this until satisfied with the output.
A Python code sample: 
The human selects 1 = Content Creator Agent for the task. The agent  executes and returns the agent_output, as shown in Figure 3.
Once satisfied with the results, move on to query the Digital Artist Agent to create artwork for social media promotion (Figure 4).
The following Python code sample uses the title generated by the Content Creator Agent as input for the image prompt:
The generated image is saved as output.jpg. 
You can iterate on the generated images to obtain different variations of artworks to get the results you’re looking for (Figure 6). Adjusting the input prompt slightly from the Content Creator Agent can yield diverse images from the Digital Artist Agent.
Finally, perform post-processing and refine the combined outputs from both agents, formatting them in markdown for final visual review (Figure 7).
In this blog post, you’ve learned how to build a human-in-the-loop AI agent using NVIDIA NIM microservices and LangGraph by LangChain to streamline content creation workflows. By incorporating AI agents into your workflow, you accelerate content production, reduce manual effort, and retain full control over the creative process.
NVIDIA NIM microservices enable you to scale your AI-driven tasks with efficiency and flexibility. Whether you’re crafting promotional messages or designing visuals, human-in-the-loop AI agents provide a powerful solution for optimizing workflows and boosting productivity.
Learn more with these additional resources: 

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