When @grok doesn’t reply in your replies so you have to backchannel for an answer.
AGI
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Interview Martin Advisory Board Member CUSP AI
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Watch the whole interview with Martin, a valued member of the @cusp_ai advisory board, on YouTube now:
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Self-Improving AI Loops: Why Verifiers Matter Most
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I went deeper on this in the full video: how self-improving AI actually works, where these loops break, and why the verifier matters more than most people think.
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Agent Autonomy: Design Through Feedback Loop Verification
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So the practical question for builders is not: “How do I make my agent more autonomous?” It is: “What feedback loop am I letting it optimize?” Because once the loop starts running, your verifier becomes the steering wheel.
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The Verification Problem in AI Self-Improvement Systems
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AI agents can now generate endless ways to improve themselves. New prompts. New code. New plans. New experiments. New tool calls.
That is not the bottleneck anymore.
The bottleneck is the verifier.
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Agent Deployment via MCP, A2A and Remote Graph API
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As part of this deployment, you can interact with the agent in several ways We expose the agent via MCP and A2A, as well as with as a "remote graph" API, which means you can interact with as you would a normal in-memory graph See streaming docs here: https://
docs.langchain.com/langsmith/stre
aming
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Agent Latency Optimization: Router and Manager Bottlenecks
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Agreed on both. Latency depends mostly on the agent blocks, not the connections themselves (those are basically free). With a decent local model the bottleneck is inference time per agent call, so the router and manager agents dominate. Most of the 29 edges are just data
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Agent Environment Architecture: Memory Routing Integration
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Thanks! That framing is exactly how I started seeing it too. Once memory, routing, and multi-channel sit in one place, the workflow stops feeling like a script and starts feeling like an environment the agents actually live in.
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Dual Memory Architecture: Short-term Manager and Long-term Supabase Retrieval
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Hey, there are two memory layers running in parallel. Short-term lives on the manager agent (keyed to the user). Long-term embeds each turn into Supabase and pulls relevant past context back before the model sees the new message. State lives outside the model, so swapping
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Hermes AI Agent: From Concept to Functional AI Agent
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Hermes AI Agent isn't an overnight success. It's the gap between a concept and something that ACTUALLY WORKS. An Agent with skills that grows with you.