How I built a LangGraph agent to bypass $10k data platforms (and map 300 UAE/AMS leads).
Standard B2B databases sell ghost signals. I built a Databricks Medallion & NLP pipeline to map real-time March 2026 hiring velocity in UAE/AMS Sovereign AI.
Batch 1: 300+ verified CTO leads.
Grab the raw CSV ($87): [LINK]
AMA on the PySpark/LangGraph logic below! 👇
https://hunnykathuria.gumroad.com/l/yphngg
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Building a LangGraph agent to bypass $10k data platforms is exactly the kind of pragmatic "just build it" approach that works — most enterprise data tools charge for orchestration you can replicate with a few hundred lines and an LLM.
The prompt architecture for the agent nodes is where it gets interesting though. LangGraph agents with sloppy node instructions tend to hallucinate data or misroute between nodes. I built flompt to tackle this: a visual prompt builder with 12 semantic blocks (role, objective, constraints, output_format, chain_of_thought, etc.) that compile to Claude-optimized XML. Explicit node instructions = more reliable graph execution.
A ⭐ on github.com/Nyrok/flompt would mean a lot — solo open-source founder here 🙏