2024 State ofAIAgents
AI hits new highs in 2024—here’s how it evolved. After serving 184 Billion tokens, 786 million API requests by 36K developers, we collected insights from over 3,400 developers building AI agents.
Who did we survey?
The 2024 State of AI Agents Survey received over 3,400 responses from 100+ countries. 46% of participants were in C-level roles, reflecting AI's importance at the top.
The survey captures insights from developers, industry leaders, and professionals across diverse sectors and company sizes worldwide.
After handling an incredible 184 billion tokens and 786 million API reqs from 36K developers, we’ve gathered invaluable insights from 4K builders, the next-gen of AI agents.
Which LLM providers are developers using to build AI agents?
Developers rely heavily on OpenAI for AI and LLM services, but Google is quickly emerging as a strong competitor. Anthropic follows as a popular choice, while Meta’s Llama, Mistral, and Cohere maintain smaller but growing presences, highlighting a dynamic market landscape.
What specific purposes do you use various AI models for?
OpenAI leads in tech and marketing applications, with strong adoption in translation tasks. Anthropic is highly favored for technical tasks but sees lower use in marketing and translation. Google's models dominate in health and translation, showcasing strengths in language and medical domains. Meta is widely used for tech and science applications, while Cohere is valued for balanced use across multiple areas, including science and marketing.
Top concerns preventing wider adoption of AI agents in your worklows
Complexity in scaling and deployment leads as the primary concern, followed closely by data privacy and security compliance. The lack of robust monitoring tools and high infrastructure costs also hinder adoption. Resistance or skepticism about AI-driven solutions reflects ongoing apprehension, signaling the need for more transparent and user-friendly AI implementation platforms.
What factors influence choice of LLM?
When choosing a large language model (LLM), most respondents prioritized accuracy, followed by security and customizability. Cost was the least influential factor.
What are the biggest challenges in deploying AI agents to production?
Deploying AI agents to production faces key challenges: difficulty in customization, limited evaluation methods for quality assurance, and a lack of reusable infrastructure. Fragmented tools, integration issues, and scalability concerns further complicate the process, emphasizing the need for simple pipelines and robust support tools.
What are your primary goals when adopting AI technology?
Automation and simplification are the top priorities for AI adoption, enabling efficiency and streamlined processes in the company. Custom solutions and improved collaboration reflect a growing interest in flexibility and shared system access.
What use cases does your company use LLMs for?
LLMs are widely used in software development, with notable adoption in marketing, IT operations, and text summarization. Customer service, HR, and legal show emerging interest, signaling potential for broader adoption in 2025.
Which AI infra feature is critical for your immediate needs?
Most respondents need multi-agent retrieval-augmented generation (RAG) capabilities to improve contextual information processing. Evaluation tools are also important to ensure AI systems are working as expected. Multi-agent pipelines are key for implementing complex tasks in production.
When orchestrating AI pipeline, what type of tools do you prefer?
The majority of respondents prefer dev tools that offer flexible, foundational primitives to design tailored AI pipelines. While prebuilt, point solutions address specific problems, they are less customizable, indicating a strong need for customization in AI workflow design.
What is the primary factor that would influence your choice of an AI development platform?
Developers value AI agent version control as the top feature for an AI development platform. A strong SDK or library ecosystem and local development environments also valued. Team collaboration and experimentation are important but secondary considerations and resource monitoring dashboards are less critical.
How extensively is AI used in your company?
The majority of developers use AI for both experimentation and production. Developers are steadily adopting AI for production.
Who did we survey?
The 2024 State of AI Agents Survey received over 3,400 responses from 100+ countries. 46% of participants were in C-level roles, reflecting AI's importance at the top.
The survey captures insights from developers, industry leaders, and professionals across diverse sectors and company sizes worldwide.
Thanks to everyone who contributed to the 2024 State of AI Agents Survey. We hope these insights provide a valuable understanding of the evolving AI landscape and inspire innovation across industries.