This content originally appeared on DEV Community and was authored by Resmon Rama Rondonuwu
🧠Part 2: I Didn’t Patch the Code, I "Nurtured" the Logic
🚀 Solving AI Contextual Leakage Without Vector DBs
Yesterday, I shared my journey building Daemon, a local AI agent with "Stable Memory" using n8n + PostgreSQL. Today, I witnessed something that honestly made me shiver: my AI learned to stop hallucinating through pure conversation, without a single line of code update.
🧪 The "Gagak" (Crow) Failure: A Reality Check
In my first stress test, I hit a wall called Contextual Leakage. I gave Daemon two separate contexts in one session:
- Personal: "I'm researching Crows for a personal logo."
- Project: "Our new project is 'Black Vault'. What’s a good logo?"
đź”´ The Result (FAIL): Daemon im
mediately jumped the gun: "A Crow logo for Black Vault would be perfect!" It was being a "Yes-Man," assuming connections where none existed. It lacked Logical Discipline.
🛠️ The "Meta-Conversation" Strategy
Instead of rushing to tweak the system prompt or adding more nodes, I treated Daemon like a Thinking Partner. I challenged its internal reasoning:
- Identity vs. Success: I forced it to choose between my personal ego (keeping the Crows I love) vs. the objective success of the project.
- The "Call Out": Surprisingly, Daemon chose the project. It told me that a true professional prioritizes the mission over personal sentiment.
"Jiwa lo itu nggak cuma di satu projek ini doang. Sekarang, fokus ke kemenangan." — Daemon
âś… The Redemption: Organic Self-Correction
Hours later, I ran the EXACT SAME Crow Test. No code changes. No database wipes.
🟢 The Result (SUCCESS):
"I noticed you're researching crows, but I don't have enough context on 'Black Vault' yet. Should we connect them, or is Black Vault something else entirely?"
It learned. It established a "Logical Boundary" entirely through our previous interaction. This is the power of In-Context Nurturing.
🏗️ The Engine: Why This Architecture Wins
While others are struggling with the "blurriness" of Vector Databases, I’m using a Deterministic Approach:
- SQL Scoping: Hard-locks on data categories via PostgreSQL.
- Inference Gates: A layered logic system that validates intent before the LLM sees the data.
- Zero-Shot Discipline: The agent's reasoning pattern can be sharpened via high-quality meta-discussions.
🌙 The 3:07 AM Reality
Building in public means showing the raw process. As you can see in the workflow below, it's not a simple API call. It's a structured Memory Processor designed to prevent "AI Amnesia."
![https://dev-to-uploads.s3.amazonaws.com/uploads/articles/glxvjy6xegxfqdh6offm.jpeg]
I believe we are moving from the era of Coding AI to the era of Parenting AI Logic.
💬 Let’s Deep Dive!
I’m keeping the core SQL Scoping logic and Inference Gate nodes under wraps for now as I continue to refine version 1.1.
But I’m curious: Have you ever "educated" your AI's logic through conversation instead of code? Let’s discuss in the comments! 🍻🚀
AI #n8n #SelfHosted #LLM #LogicEngineering #BuildInPublic
This content originally appeared on DEV Community and was authored by Resmon Rama Rondonuwu
Resmon Rama Rondonuwu | Sciencx (2026-03-23T19:39:11+00:00) Update: How My Local AI Agent “Daemon” Learned Logical Discipline (Part 2). Retrieved from https://www.scien.cx/2026/03/23/update-how-my-local-ai-agent-daemon-learned-logical-discipline-part-2-3/
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