Here are the six most important terms you should know if you're working with agentic AI:
π πΌπ±π²πΉ ππΌπ»ππ²π π π£πΏπΌππΌπ°πΌπΉ (π ππ£)
A standardized way for AI systems to access and interact with external data sources and tools. Think of it as a universal adapter that lets agents communicate with different services consistently.
ππ΄π²π»π π¦πΈπΆπΉπΉπ
Pre-built capabilities that coding agents can use to write better code. Weaviate's Agent Skills repository (https://t.co/pkUIDxkRVb) is a great example - it bridges coding agents like Claude Code, Cursor, and GitHub Copilot with Weaviate's infrastructure, so your agent gets the right context for cluster management, data imports, and search operations.
ππ΄π²π»ππΆπ° π₯ππ
RAG pipelines that incorporate AI agents into the retrieval process. Unlike vanilla RAG's sequential flow, agentic RAG uses agents to route queries to specialized knowledge sources, validate retrieved context, and even reformulate queries.
π¦πΆπ»π΄πΉπ² ππ΄π²π»π ππΏπ°π΅πΆππ²π°πππΏπ²
The simplest agentic setup - essentially a router. You have multiple knowledge sources (databases, APIs, tools), and one agent decides which to query based on the user's request. Clean and straightforward.
π ππΉππΆ ππ΄π²π»π ππΏπ°π΅πΆππ²π°πππΏπ²
Multiple specialized agents working together, each handling specific tasks. Orchestration frameworks like CrewAI can help coordinate these agents, managing the handoffs and ensuring everything works together smoothly.
π π²πΊπΌπΏπ
The component that πππΌπΏπ²π π°πΌπ»ππ²π π, prior interactions, and data collected during task execution. Includes both short-term memory (in the context window) and long-term memory (retrieved on demand). Big differentiator in how well an agentic system works, especially in multi agent systems.
Did I miss any terms people should definitely know? Drop them in the comments π½ π
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