Every AI, email, and agent term explained for developers. From context engineering to DKIM.
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A large group of AI agents operating in parallel with minimal central coordination, often self-organizing around a shared objective.
A prompting technique that instructs an LLM to reason through a problem step by step before producing a final answer.
A memory system that stores and retrieves specific past experiences or interactions, giving an AI agent the ability to recall what happened in previous sessions.
Constraints and safety mechanisms built around AI agents to prevent harmful, off-topic, or unauthorized behavior.
A design pattern where an AI agent pauses its workflow to get human approval, review, or input before proceeding with a critical action.
Architectures where multiple AI agents work together, each handling specialized tasks, to accomplish goals that a single agent cannot.
A central AI agent that coordinates, delegates, and manages tasks across multiple sub-agents in a multi-agent system.
An agent architecture where the model alternates between reasoning about a situation and taking actions, using observations from each action to inform the next step.
An AI agent that does not retain memory between interactions, treating each request as independent with no prior context.
A hidden instruction given to an LLM before the user's message that defines the model's role, behavior, constraints, and personality.