Agentic AI Part III - From Concept to Capital
Explore how to build and fund an Agentic AI startup, from identifying niches to fundraising strategies
The AI landscape is evolving faster than ever, with Agentic AI systems leading this transformation. These autonomous AI agents are no longer just responding to queries—they’re managing complex tasks and making decisions, often without any human guidance. Take Klarna, for example: in just one month, their AI-powered assistant, built using OpenAI, managed two-thirds of their customer service conversations, performing the work of 700 full-time employees.
This level of adoption shows that businesses are undergoing a profound shift in how they operate and engage with technology. For entrepreneurs looking to enter the AI space, building an Agentic AI startup offers enormous opportunities, but it also comes with its own set of challenges. Let’s look at some key strategies for creating and securing funding for an Agentic AI startup in today’s fast-paced environment.
What is Agentic AI?
Agentic AI represents a new era in artificial intelligence. These systems are not just reactive, but proactive, capable of performing tasks, making decisions, and adapting to their surroundings autonomously. Unlike traditional AI, which simply processes input, Agentic AI can take the initiative and pursue specific objectives. This shift holds the potential to revolutionize sectors ranging from customer service to finance, healthcare, and beyond.
Steps to Building an Agentic AI Startup
Find Your Focus Area
To start your Agentic AI venture, it’s essential to identify a specific niche or problem where your technology can have a major impact. Rather than aiming to create a broad, general-purpose AI, the most successful AI startups focus on solving high-value, vertical-specific problems where automation and intelligence can drive significant change.
Choose the Right Tech Stack
The technology foundation of your startup is critical. Consider options like open-source platforms (e.g., PyTorch or TensorFlow), custom machine learning models, or building on top of large language models (LLMs). Your decision should align with your team’s skill set and the demands of your specific use case.
Assemble a Talented Team
Building the right team is crucial. You’ll need AI and machine learning engineers, data scientists, developers, and experts in your target industry. A strong balance between technical talent and business know-how will help you turn your innovative idea into a viable business.
Prototype and Validate Early
Rapid prototyping will help you test ideas and validate your core value proposition. Leverage low-code tools and minimum viable products (MVPs) to gather early feedback from potential customers, and ensure you’re addressing real pain points through customer interviews, surveys, and research.
Establish MLOps Best Practices
As your product matures, implementing strong machine learning operations (MLOps) practices is essential for scalability. This includes systems for version control, monitoring, and managing models to keep your AI agents performing optimally as they scale.
Securing Investment for Your Agentic AI Startup
Show Early Traction
Investors are looking for evidence that your solution is viable and in demand. Early traction—whether through pilot projects, beta testing, or partnerships—will significantly improve your chances of securing funding.
Emphasize Your Unique Value
Clearly differentiate your AI technology from competitors. Highlight any proprietary data, algorithms, or specialized knowledge that makes your solution particularly valuable in the market.
Fundraising in Stages
Start small, with a pre-seed or seed round to fund early development and customer discovery. Move to a Series A when you have initial traction to fuel your startup’s growth and expansion.
Find Investors with Relevant Experience
Target investors who have experience with AI and understand your market. Their expertise will not only help secure funding but also provide valuable guidance and support.
Prepare for In-Depth Due Diligence
Be ready to explain your technology comprehensively. Investors will be looking closely at your approach to data privacy, ethical AI practices, and how you manage your models' reliability and performance.
Challenges and Opportunities for Agentic AI Startups
Building an Agentic AI startup is no small feat, with unique obstacles such as:
Technical Complexity: Address issues like model degradation and scalability.
Talent Competition: Hiring the best AI talent can be tough, so offer a compelling reason for top talent to join your team.
Ethical AI: As AI becomes more autonomous, ensuring responsible AI behavior and addressing biases are crucial.
Regulatory Compliance: Stay ahead of evolving regulations around AI to ensure you meet all requirements.
The Future is Bright for Agentic AI Startups
Though still emerging, Agentic AI offers unprecedented opportunities. With a focus on real-world problem-solving, building solid technology, and assembling a strong team, your startup can thrive in this space. The investment climate is ripe—generative AI alone saw $29.1 billion invested across 700 deals in 2023, a 260% rise from the previous year.
Success will depend on more than just innovative technology. Applying it effectively to address real-world problems is key. Agentic AI systems, which can perform multi-step tasks and interact with other systems in real-time, have the potential to dramatically reshape industries.
At Untapped Ventures, we are excited about the transformative potential of Agentic AI and are always looking to partner with visionary founders pushing the boundaries of this revolutionary technology.
As AI systems continue to evolve, this moment will be remembered as a tipping point. Investors like Grace Isford from Lux Capital note a "dramatic increase" in interest in AI agent startups. The future of AI is agentic, and the time to act is now. The revolution is just beginning.