Hermes Agent + Mixture of Agents is insane…

Introduction to Mixture of Agents in Hermes Agent

What is Mixture of Agents?

• Hermes agent has introduced a new feature called "mixture of agents," enabling access to frontier-level intelligence without needing Fable or GPD 5.6.

• This feature consults multiple AI models (e.g., Grog, GPD, Gemini, Opus) and aggregates their responses through a powerful single model for optimal results.

• The mixture of agents can outperform the best publicly available models like GBD 5.5 and Opus 4.8 by effectively combining different AI capabilities.

Importance of Mixture of Agents

• The current challenge in the AI industry is that companies are withholding their best models from public release, necessitating innovative solutions like mixture of agents.

• Users can select which models and providers to utilize based on their existing subscriptions, allowing for cost-effective implementation.

When to Use Mixture of Agents

Appropriate Use Cases

• Mixture of agents is ideal for complex tasks such as debugging, code review, and security hardening rather than simple queries where it may be overkill.

• Users can create multiple presets tailored for specific tasks (e.g., adding features or conducting code reviews), enhancing flexibility in application.

Clarification on Terminology

Distinction Between Terms

• It's crucial not to confuse "mixture of agents" with "mixture of experts," which refers to an architecture involving specialized models focusing on different domains (e.g., math vs. coding).

• In contrast, mixture of agents involves separate AI models working independently before aggregating their outputs into a final response.

Considerations for Using Mixture of Agents

Key Insights

• Utilizing more models increases tool calls and token usage; thus, this approach is not cost-efficient for quick tasks but aims at maximizing output quality.

• Chrome cache remains intact during operations with mixture of agents, ensuring efficiency in data handling.

Setting Up Hermes Agent

Setup Process Overview

• The recommended setup environment for Hermes agent is a VPS (Virtual Private Server), allowing continuous operation even when personal computers are off.

• Hostinger offers dedicated presets for easy setup; users should choose plans that accommodate running multiple agents efficiently.

SSH Access and Terminal Management

Managing VPS via SSH

• After setting up the VPS, users will need to manage it using SSH commands through terminal applications like CMAX or default Mac OS terminal.

• Emphasizing efficiency, users should leverage AI agents to assist with managing setups instead of manually executing every command themselves.

Acquiring API Keys

Integrating Open Router

• To use various models within Hermes agent effectively, users must create an account on Open Router and obtain API keys while keeping spending limits in mind due to potential costs associated with multiple model usage.

Configuring Mixture of Agents Presets

Setting Up Reference Models

• Users can configure mixtures by selecting reference models such as GLM 5.2 and GPT 5.5 through Open Router while designating an aggregator model like Opus 4.8 for decision-making processes.

Effective Communication with AI Agents

Asking Better Questions

The key skill in utilizing AI effectively lies in articulating broader goals rather than just task-oriented instructions; this enhances the overall productivity achieved through these technologies.

Overview of AI Model Capabilities

Current State of Open Source Models

• The speaker praises the GLM 5.3 model, claiming it to be the best open-source model currently available.

• Emphasizes the convenience of using CMAX over default terminal options for project management and execution.

Project Execution with Hermes

• Discusses how Hermes creates a temporary file for prompts, showcasing its ability to generate better prompts than users can manually.

• Highlights the importance of orchestrating multiple agents through PI agent to achieve superior results compared to individual efforts.

Monitoring and Cost Management in AI Projects

Workflow Efficiency

• The speaker notes that setting up efficient systems is crucial in the era of AI agents, mentioning a current spend of $1.8 as manageable.

• Suggests starting with two reference agents instead of four for cost-effectiveness while still achieving desired outcomes.

Advocacy for Open Source Models

• Urges viewers to allocate more resources towards open-source models like Kimi and GLM, warning against reliance on companies like OpenAI and Anthropic due to profit motives.

Ethical Considerations in AI Development

Data Utilization Concerns

• Critiques major companies for hoarding knowledge extracted from public data without providing accessible models back to users.

• Calls attention to the need for individuals to learn how to utilize local and open-source models effectively.

Misconceptions About Model Quality

• Addresses misconceptions regarding models trained in China, asserting their quality is comparable or superior despite concerns about data security.

Cost Analysis and Profit Margins

Financial Implications of Using Different Models

• Compares costs between running GLM 5.2 versus Opus 4.8, highlighting significant savings with GLM usage.

• Explains that perceived subsidies from subscription services are misleading; high profit margins exist on API usage by closed-model providers.

Future-Proofing Against Closed Models

Long-Term Strategy Recommendations

• Warn against building businesses on closed models, predicting regret as access to AI becomes increasingly critical in future scenarios.

Practical Implementation Insights

• Describes how multiple agents interact within Hermes, likening it to a meritocratic system where the best ideas prevail regardless of source.

Real-Time Monitoring and Updates

Importance of Efficient Information Flow

• Stresses that concise updates from monitoring agents enhance understanding and decision-making during complex projects.

Handling Stagnation in Processes

• Illustrates how PI agent intervenes when processes stall by sending steering prompts based on real-time status checks.

Successful Deployment Outcomes

Achievements Through Automation

• Celebrates successful deployment achieved entirely through automation by Hermes without manual intervention or setup requirements.

User Experience Feedback

• Provides feedback on a deployed game (3D Flappy Bird), noting areas for improvement while confirming functionality was intact post-deployment.