I Combined 3 AI Models. The Result Surprised Me (Hermes Agent MoA)

What happens when multiple AI models work together before giving you a final answer? In this video, I test Hermes Agent’s Mixture of Agents (MoA) feature a system that lets multiple AI models independently analyze the same problem before an aggregator model combines their strongest ideas and executes the task. I’ll break down exactly how Mixture of Agents works, show you how to configure it in Hermes Agent, and put it to the test by building a complete Kanban board for a solo YouTube creator. For the demo, I use DeepSeek V4 Flash and MiniMax M2.7 as reference models, with GPT-5.4 as the aggregator. You’ll see the reference models reason independently, critique the approach, and feed their insights into the final acting model. 📝 You’ll learn: How Hermes Agent’s Mixture of Agents architecture works The difference between reference models and the aggregator How to configure multiple MoA presets How reference_max_tokens affects speed and cost How multiple AI models collaborate during a real coding task The cost and latency trade-offs of Mixture of Agents When using multiple models is actually worth it Mixture of Agents isn’t useful for every task. But for complex problems like software architecture, debugging, code reviews, migration planning, and research, getting multiple independent perspectives can produce stronger results than relying on a single model. Watch the full demo and decide for yourself: would you rather use one powerful AI model or a team of them? If you're building with AI agents, multi-agent orchestration, or just trying to get more reliable output from the models you already use, this is a feature worth understanding before you write it off as "too expensive." Try it yourself: pick a problem you already solved with a single model, run it again with Mixture of Agents, and compare the results. Let me know how it goes in the comments. ⏰ Timestamps / Chapters: 00:00 – Why Mixture of Agents beats waiting for the next model 00:44 – The CEO analogy: how MoA actually thinks 01:08 – How Mixture of Agents work in Hermes Agent? 02:25 – How to configure Mixture of Agents 03:30 – Using multiple MoA presets for different task types 04:04 – Why "reference max tokens" matters 04:46 – Live demo: building a Kanban board with /moa 05:23 – Watching reference models reason in real time 06:07 – The senior engineers analogy 06:53 – Reviewing the finished Kanban board app 08:32 – Trade-offs in Mixture of Agents explained 10:19 – When to actually use Mixture of Agents 10:45 – Mixture of Agents Challenge + final thoughts #AI #HermesAgent #Agents #Automation #Claude #Gpt #AgenticAI #MixtureOfAgents