13/04/2026
6 AI agent terms you need to know in 2026:
(Most developers still confuse #1 and #2)
๐ญ. ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ผ๐ป๐๐ฒ๐
๐ ๐ฃ๐ฟ๐ผ๐๐ผ๐ฐ๐ผ๐น (๐ ๐๐ฃ)
Think of it as "USB-C for AI" - a universal standard that lets AI applications connect to external data sources and tools. Instead of building custom integrations for every tool, MCP provides one protocol that works everywhere.
๐ฎ. ๐ฆ๐ธ๐ถ๐น๐น๐
Basically, the agentโs job description. While MCP provides the connection and Tools provide the API, a Skill is the higher-level logic that orchestrates them. It encapsulates the domain-specific reasoning needed to turn a raw tool into a finished outcome.
๐ฏ. ๐ฆ๐ถ๐ป๐ด๐น๐ฒ ๐๐ด๐ฒ๐ป๐ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ
One agent handles the entire pipeline - from understanding the task to planning steps, using tools, and generating responses. It's the simplest form of agentic system where one LLM orchestrates everything.
๐ฐ. ๐ ๐๐น๐๐ถ-๐๐ด๐ฒ๐ป๐ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ
Multiple specialized agents work together, each handling different parts of a task. One might retrieve information, another validates it, and a third generates the final response. This creates more robust and capable systems.
๐ฑ. ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ฅ๐๐
An AI agent-based implementation of RAG that goes beyond simple retrieval. The agent can route queries to specialized knowledge sources, validate retrieved context, and make dynamic decisions about what information to use.
๐ฒ. ๐๐ด๐ฒ๐ป๐ ๐ ๐ฒ๐บ๐ผ๐ฟ๐
Agents use two types of memory:
โข Short-term: Stored in the context window for immediate use
โข Long-term: Retrieved on demand from external storage (like vector databases)
This memory layer helps agents maintain context across interactions and learn from past experiences.
