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    <title>agentic ai on Sadman Kabir Soumik</title>
    <link>https://blog.sksoumik.com/tags/agentic-ai/</link>
    <description>Recent content in agentic ai on Sadman Kabir Soumik</description>
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    <copyright>Copyright © 2022, Sadman Kabir Soumik</copyright>
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      <title>Memory Systems in Agentic AI: Procedural, Semantic, and Episodic Memory</title>
      <link>https://blog.sksoumik.com/artificial-intelligence/memory-systems-agentic-ai/</link>
      <pubDate>Wed, 12 Aug 2026 18:00:00 +0000</pubDate>
      
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            Large Language Models are good at understanding text and generating answers.
But there is an important problem.
By default, an LLM does not truly remember everything that happened before.
Imagine you talk to an AI assistant today and tell it:
I prefer short answers and I usually work with Python.
Tomorrow, you start a new conversation.
If the system has no memory, the assistant may know nothing about that previous conversation.
          
          
        
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      <title>Building Multi Agent AI Systems: From Orchestration to Production</title>
      <link>https://blog.sksoumik.com/artificial-intelligence/building-multi-agent-ai-systems/</link>
      <pubDate>Thu, 15 Jan 2026 18:00:00 +0000</pubDate>
      
      <guid>https://blog.sksoumik.com/artificial-intelligence/building-multi-agent-ai-systems/</guid>
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            Building one AI agent is relatively easy.
You give an LLM some instructions, connect a few tools, and let it perform a task.
For example:
1User 2 ↓ 3Agent 4 ↓ 5LLM 6 ↓ 7Tools 8 ↓ 9Answer But imagine the task becomes much larger.
You want an AI system that can:
Understand a complex request Break the request into smaller tasks Search the web Read internal documents Write code Analyze data Ask another agent for help Remember previous work Check its own results Ask a human before taking risky actions Continue working even when one step fails One agent can try to do everything.
          
          
        
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