My Most Intricate Agentic Project

My most intricate Agentic Project thus far:

4 different AI Providers ~5 hours of implementation time (for v0) B2B product that supercharges sales

Having an overseer for all event logs creates an autonomous system where the AI Agents leverage Vector Databases to semantically match product features to prospect needs while generating hyper-personalized outreach through reference-based templates.

I'm glad to have used the industry standards for AI Agents with #ClaudeSonnet #GoogleGemini; they made the idea of an autonomous sales analysis agent possible.

#AgenticAI #Claude #Gemini #Automation #TechInsights #GenerativeAI #LLM #Agents #AIAgents

Building an Orchestrator That Actually Thinks

Traditional API chaining is just do this then that with no validation and no recovery. I built something that evaluates outputs and decides what to do next.

Why Most Agent Implementations Collapse at the Skill Layer

Agents deliver when you constrain creative capacity within executable sandboxes. Open ended prompts generate inconsistent outputs.

If Your Agent Can't Teach Itself, You're Wasting Your Time

An agent that can't teach itself contradicts itself. Build agents that improve themselves. Static ones don't survive.