AI agents are everywhere but do we actually understand the language used to describe them?
- What is the difference between an LLM, an assistant and an agent?
- What do autonomy, orchestration, MCP, RAG and agent memory mean?
- And what happens when an agent experiences prompt injection, tool poisoning, scope creep or memory poisoning?
Here is the full Playlist - https://www.youtube.com/watch?v=T3piL2E6ALc&list=PLfpf_ZW3UBY4
LLM, Assistant, Agent aur Agentic AI
Goal , Task , Instruction aur Autonomy
Agent Loop, Planning aur Reasoning
Tools, Function Calling aur Orchestrator
MCP, A2A aur Multi Agent Systems
Video 6 - Context RAG aur Memory
Non Human Identity aur Trust Boundaries
Prompt Injection, Goal Hijacking aur Tool Poisoning
Context, Memory aur RAG Poisoning
Excessive Agency, Scope Creep aur Confused Deputy
Tokens, Supply Chain aur Shadow Agents
Agentic Security Controls
In my latest videos, I break down 30 important AI-agent and agentic-security terms in simple, human language. No complicated jargon. No dictionary-style definitions. Just clear explanations, practical examples and the security questions we should be asking.
Because once AI can use tools, access data, remember information and take action, security can no longer be treated as an optional guardrail.
The video is available in conversational Hinglish.
💬 Which term should I explain in greater depth next? 📌 Save this as your agentic-security glossary. 🔁 Share it with someone building or securing AI agents.
#AIAgents #AgenticAI #AISecurity #AgenticSecurity #Cybersecurity #GenerativeAI #MCP #MCPSecurity #PromptInjection #ToolPoisoning #ArtificialIntelligence