How to build AI agents without coding experience
Low-code tools are making AI agents accessible to everyone
This feature is an excerpt from my free newsletter, Building AI Agents. If you’re an engineer, startup founder, or businessperson interested in the potential of AI agents, check it out!
At first glance, AI agents seem like magical alchemy to many non-coders. But a growing number of low-code tools are making them not only accessible to those outside of software engineering, but easy.
The first true LLM agents, such as AutoGPT and BabyAGI were demoed in the weeks following the release of GPT-4 in March 2023. These systems were inflexible proofs-of-concept with built-in tools and defined workflows, with limited to nonexistent customization options. While users could assign tasks to the agent, such as “write me a report on foreign exchange markets,” changing its functionality in any meaningful way required extensive software engineering knowledge and hours of work.
This began to change as agent frameworks emerged that allowed coders to define their own agents in Python, equip them with custom tools, and arrange them into arbitrary workflows with multiple agents interacting to accomplish tasks. Packages such as LangChain and AutoGen gave programmers the ability to assemble modular components into increasingly sophisticated multi-agent systems. Nevertheless, these packages still required fluency in Python, keeping agents beyond the reach of many of the knowledge workers who would most benefit from their capabilities.
In parallel, however, a new class of agent framework was born: low-code. Low-code tools were not new — their use for workflow automation was a significant trend in the mid-late 2010s — but coupling them with LLMs gave them extraordinary new power. As with the transformation of robotic process automation (RPA), AI agents gave a struggling field the key component it needed to begin changing the face of enterprise work.
In a standard low-code agent application, components of an agentic system, such as LLM calls, databases, web APIs, and more are represented as blocks on a canvas, which can be chained together to create workflows. Rather than convoluted and scattered lines of code, the flow of data through an agent application can easily be visualized step-by-step.
Some of these systems exist as standalone applications, such as n8n, Flowise, Langflow, and Lyzr. In many cases, they allow users to insert custom components that incorporate Python, JavaScript, or other code — thus, while users do not need to be programmers to use them, they have the option to collaborate with programmers to add integrations and functionality.
Others are the low-code implementation of existing code-based frameworks, including AutoGen, CrewAI, and LangGraph. Each of these dominant Python agent frameworks has a corresponding “studio” application that enables workflows to be constructed without code and run directly from the low-code application or exported, allowing non-programmers to build agentic apps and then pass them to software engineers to be put into production.
Finally, enterprise agent providers such as Microsoft, Salesforce, and a host of RPA companies are creating their own agent studio applications, making agentic AI accessible to users of their platforms.
In an interview last year, NVIDIA CEO Jensen Huang made a stir with his bold claim that companies of the future would “employ” thousands of AI agents for every human. As this dream comes closer to reality, it is increasingly clear that the builders of these agents will not be tiny teams of specialized software engineers, but the everyday employees whose work they will be streamlining.
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