Dragon

I make AI agents less stupid | Building in public because bugs need witnesses

[Dragon @Dragoncoderx]

@YashKumarP93601 Love to connect

[Dragon @Dragoncoderx]

Want to become an AI Engineer but don’t know what to learn first?

Don’t try to learn everything.

Pick a path and go deep. 👇

1️⃣ ML Engineer
2️⃣ Deep Learning Engineer
3️⃣ GenAI Engineer
4️⃣ LLM Engineer
5️⃣ RAG Engineer
6️⃣ AI Agent Engineer
7️⃣ MLOps Engineer
8️⃣ Computer Vision Engineer
9️⃣ NLP Engineer
🔟 AI Data Engineer

For every path:

Learn → Build → Deploy → Iterate

The mistake I see often is collecting tools without building anything.

Pick one direction.
Build real projects.
Learn what you actually need along the way.

Depth beats a giant checklist.

Save this for your AI learning roadmap.

#AI #AIEngineering #MachineLearning #GenAI #LLM #AIAgents #MLOps #RAG

[Dragon @Dragoncoderx]

SONNET 5.5 Is here to destroy 🥶

[Dragon @Dragoncoderx]

Gemini 4 ARGON vs GPT-6 LUNA

[Dragon @Dragoncoderx]

#RAG
COMPLETE RAG SYSTEM
@GoogleAI @Gemini @ChatGPT @rag
@webdevjob @workfromhomejobs
#rag
#ml
#rag
#models
#llm
#vectordb

[Dragon @Dragoncoderx]

#fullstackdevelopment
#webdevelopment
#ai
#ml
#chatgpt
#gemini

[Dragon @Dragoncoderx]

🚀 Game-changer alert! Gemini 4 Argon, the latest frontier AI model, is here!
​🛡️ It’s rolling out to cyber defenders STARTING TODAY, with a wider release coming ASAP.
​💡 Incredible progress and amazing introductory pricing! Check the diagram for details! 👇

[Dragon @Dragoncoderx]

┌────────────────────────────────────────────────────────────────────────┐
│ HUMAN / USER PROMPT │
└──────────────────────────────────┬─────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────────────────┐
│ REASONING & PLANNING ENGINE │
│ ┌───────────────────────┐ ┌────────────────────────────┐ │
│ │ ReAct Loop │ ◄─────────► │ Plan-And-Execute │ │
│ │ (Thought ──► Act) │ │ (Task Decomposition) │ │
│ └───────────────────────┘ └────────────────────────────┘ │
└──────────────────┬──────────────────────────────────┬──────────────────┘
│ │
▼ ▼
┌──────────────────────────────────────┐ ┌──────────────────────────────┐
│ MEMORY SYSTEM │ │ TOOL EXECUTION │
│ ┌────────────────────────────────┐ │ │ ┌────────────────────────┐ │
│ │ Short-Term: Context Window │ │ │ │ Function Calling / APIs│ │
│ ├────────────────────────────────┤ │ │ ├────────────────────────┤ │
│ │ Long-Term: Vector DB / Graph │ │ │ │ Code Execution Sandbox │ │
│ └────────────────────────────────┘ │ │ └────────────────────────┘ │
└──────────────────┬───────────────────┘ └──────────────┬───────────────┘
│ │
└──────────────────┬──────────────────┘
│
▼
┌────────────────────────────────────────────────────────────────────────┐
│ EVALUATION & REFLECTION │
│ ┌───────────────────────┐ ┌────────────────────────────┐ │
│ │ Self-Correction Loop │ ◄─────────► │ Human-in-the-Loop Gate │ │
│ └───────────────────────┘ └────────────────────────────┘ │
└─────────────────────────────────────┬──────────────────────────────────┘
│
▼
┌──────────────────────────┐
│ FINAL ANSWER │
└──────────────────────────┘

[Logan Kilpatrick @OfficialLoganK]

Introducing Gemini 4 Argon, our new frontier model, rolling out to cyber defenders starting today, and more widely as soon as possible. I am really excited by the progress we have made here. Argon is priced at $2 in and $10 out during introductory pricing!

[Dragon @Dragoncoderx]

Core Pillars of AI Agent Architecture
Perception & Input Processing
Multimodal ingestion (Text, Audio, Vision, Structured Data)
Prompt parsing & contextual retrieval (RAG optimization)
Reasoning & Planning (The Brain)
ReAct Pattern: Interleaving reasoning (thought) and action execution.
Planning Strategies: Tree-of-Thoughts (ToT), Chain-of-Thought (CoT), and reflection/self-correction loops.
Task Decomposition: Breaking complex user goals into sequential sub-tasks.
Memory Systems
Short-term: Active context window management & scratchpads.
Long-term: Episodic and semantic storage using vector databases & graph networks.
Tool Orchestration & Action Execution
Function calling & API integrations.
Dynamic code execution (sandboxed Python interpreters, web scraping, database queries).
Human-in-the-Loop (HITL) approval gates for sensitive actions.
Recommended Learning Path
Level 1: Fundamentals — Master function calling with raw LLM APIs (OpenAI / Anthropic / Google Gemini API).
Level 2: Framework Mastery — Build with Agentic frameworks like LangGraph, CrewAI, or AutoGen to handle stateful multi-agent workflows.
Level 3: Production Engineering — Implement agent tracing/evaluations (LangSmith, Phoenix), error handling, latency optimization, and memory management.

[Dragon @Dragoncoderx]

In Retrieval-Augmented Generation (RAG) and Agentic AI architectures, Indexing and Vector Agents are foundational components responsible for structural knowledge management, fast semantic search, and persistent memory.

[Dragon @Dragoncoderx]

Workflow of RAG

[Dragon @Dragoncoderx]

Rag vs Agentic Rag

[Dragon @Dragoncoderx]

Ai agents

[Dragon @Dragoncoderx]

AI Agents with MCP (Model Context Protocol)

Connecting agents to external tools, databases, APIs, files, and applications.
Very useful for practical agent projects.

[Dragon @Dragoncoderx]

ICAI & Artificial Intelligence in Accounting / Finance
​AURA Training Program: ICAI has launched the AURA (AI Understanding for Rising Achievers) initiative to upskill CA students and members in emerging technology and data analytics.
​Core Curriculum Integration: Direct embedding of AI, automated auditing tools, and machine learning models into the mainstream CA curriculum.
​AI-Driven Audit & Fraud Detection: Increasing adoption of machine learning tools for real-time anomaly detection, forensic accounting, automated reconciliation, and predictive risk scoring.

[Dragon @Dragoncoderx]

Agentic & System Architecture
​Agentic Frameworks & Harnesses: Building autonomous AI agent workflows that can independently plan, execute code, call external tools, and iteratively debug outputs.
​Graph RAG & Hybrid Retrieval: Combining Knowledge Graphs with vector search (Retrieval-Augmented Generation) to give LLMs structured, deterministic context with reduced hallucination.

[Dragon @Dragoncoderx]

Advanced RAG (Retrieval-Augmented Generation): Transitioning from simple vector searches to complex GraphRAG (Knowledge Graph + RAG) and hybrid vector/lexical systems to improve context awareness and eliminate model hallucinations.

[Dragon @Dragoncoderx]

Advanced RAG (Retrieval-Augmented Generation): Transitioning from simple vector searches to complex GraphRAG (Knowledge Graph + RAG) and hybrid vector/lexical systems to improve context awareness and eliminate model hallucinations.

[Dragon @Dragoncoderx]

Multi-Agent Orchestration Systems: Moving beyond single-prompt execution to specialized teams of agents (e.g., orchestrator, executor, evaluator models) operating collaboratively via frameworks like AutoGen, CrewAI, and LangGraph.