The Future of AI Agents in Health and Nutrition
Vertical AI agents are set to dominate real-world applications, offering domain-specific solutions with tailored UX. Learn from Welling's health AI agent journey.
By 2035, over half the global population is expected to be overweight, costing the world economy an estimated $4 trillion. AI advancements, particularly with LLMs like GPT-4o, Claude 3, and Gemini 1.5 Pro, are paving the way for intelligent, domain-specific agents. These agents promise to transform healthcare from treatment to prevention, offering personalized and proactive guidance.
Why Health AI Agents Matter
Imagine a billion people with access to an always-on AI nutrition coach. Diets would improve, chronic disease rates would fall, and healthcare would shift focus. However, today’s agents are mostly generic copilots or chatbots. Truly useful, vertical-specific agents that operate within a domain and deliver consistent, meaningful action are still rare.
Designing AI Agents from First Principles
Building effective agents requires rethinking traditional apps from the ground up. For example, Welling, a health-focused agent, didn’t just upgrade existing calorie-tracking apps like MyFitnessPal with AI. Instead, it reimagined the entire experience, asking: If language models were the default UI, how would a nutrition coach work?
The UI of an AI Agent
While chat is the most intuitive interface, pure chat interfaces like WhatsApp or Telegram are limiting. Modern agentic UIs are evolving into hybrid interfaces—chat-driven bases layered with dashboards and dynamic components. For instance, ChatGPT now includes commerce and shopping cards in its chat. This new design paradigm comes with challenges:
- How to separate input (e.g., chat) from output (e.g., dashboards) without confusing the user?
- Should the agent decide or the user? For example, should water intake be logged automatically or manually?
- Should all input data go through the agent, or should some be manual?
Solving the Messy Problem Space of Language
LLMs excel with language, but humans are often vague. Had a bit of curry rice isn’t a database entry—it’s a probabilistic phrase. Vertical agents need to build their own logic layer to handle domain-specific use cases. Welling, for instance, built a food parsing engine using context-aware prompts, search algorithms, and user feedback to ensure reliable calorie and macro estimations.
Deterministic Outputs from Probabilistic Systems
Foundational models are probabilistic, but users in domains like health, legal, or finance expect consistency. Welling ensures the same input (e.g., ham, egg, tomato sandwich) yields the same calorie estimate every time. This requires sophisticated guardrails and deterministic logic layers.
Memory and Context: What to Store
With expanding context windows (e.g., Gemini 2.0’s 1M tokens), the challenge isn’t storage capacity but deciding what to store and how to process it. For example, when a user asks about breakfast, should Welling include 30 days of eating history or an abstracted Food Preferences layer? Tools like Zep and LlamaIndex help manage long-term memory and data access.
Navigating a Fast-Moving Industry
AI evolves rapidly. Token costs have dropped by >70% in 12 months, and model performance doubles every 6–9 months. Smart teams focus on:
- Will improved foundational models solve this challenge soon? If yes, deprioritize it.
- What adds to our moat—UX, proprietary data, or user network effects?
The Future: Vertical AI Agents
General-purpose agents won’t dominate; purpose-built, vertical-specific agents will. These agent-native startups will invent new UX patterns, rethink system architecture, and make novel tradeoffs. The industry is still figuring it out, but the potential is immense.
Philip Man is Cofounder and CEO at Welling, building AI for health and nutrition coaching.
Featured image: Hrushi Chavhan on Unsplash
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About the Author

Michael Rodriguez
AI Technology Journalist
Veteran technology journalist with 12 years of focus on AI industry reporting. Former AI section editor at TechCrunch, now freelance writer contributing in-depth AI industry analysis to renowned media outlets like Wired and The Verge. Has keen insights into AI startups and emerging technology trends.