I've been playing around with building my own AI agent for a while. Because I'm focusing on complex, self-referential loop emergent intelligent flows rather than business workflow software engineering flows, whether building on Claude Desk or n8n, I've encountered two pain points: 1. Selecting, accessing, debugging, and managing credentials for the MCP server/API; 2. Implementing and debugging the code for complex, self-referential loop routing between agents embedded in the workflow. This type of emergent intelligent flow agent is far superior to directly chatting with LLM models and business workflow agents in investment analysis and writing. Sentient GRID's architecture is even more advanced, acting as a massive, complex, self-referential loop emergent intelligent network. It supports collaboration between AI agent loops and humans-in-the-loop, verification of AI inference outputs, provision of compute resources for fine-tuning/inference, token-level agent task planning and routing, and more. Over time, after multiple rounds of self-evolution, Sentient GRID has a reasonable chance of becoming humanity's first open-source AGI superintelligence agent. Betting on things that most people don't yet understand is the key to achieving alpha.

Someone already has it, but they haven’t given me this tap water yet @SentientAGI

Better than n8n?

sigh

What about tools or APIs?

The system is integrated, eliminating the need for custom integration. For example, market data, market sentiment, real-time on-chain data (Chainbase), and financial intelligence (Arkham) are all integrated.