AI Agent Terminology: A Developer’s Guide to Core Concepts

AI agents are moving beyond traditional chatbot experiences. Instead of simply generating a response, an AI agent can interpret a goal, reason through multiple steps, use external tools, retrieve information, maintain state, and take actions toward a defined outcome.

For developers building AI-powered and B2B crypto applications, understanding the terminology behind these systems is essential. Concepts such as LLMs, agents, tool calling, function calling, agent loops, context, memory, RAG, embeddings, vector databases, orchestration, planning, guardrails, human-in-the-loop, observability, and evaluation form the foundation of modern agent architectures.

How AI Agents Work

An agent typically follows an iterative process:

Goal → Observe → Reason → Choose Action → Call Tool → Receive Result → Evaluate → Continue or Finish

Unlike a basic chatbot, an agent can query blockchain networks, analyze transactions, retrieve documentation, check risk data, and combine the results before producing an outcome.

Key Concepts Developers Should Know

LLM: Acts as the reasoning and language layer, helping determine what the user needs, which tools to use, and how to interpret results. It shouldn't be regarded as a real-time data source.

Tools and Function Calling: Enable the model to request preset application functions while leaving actual execution to developer-controlled application logic.

Context & Memory: Context represents what the model can see during the current interaction, while memory allows useful information to persist beyond a single model call or session.

RAG and Vector Databases: RAG gathers important external information and gives it to the model, while embeddings and vector databases facilitate semantic retrieval.

Orchestration & Multi-Agent Systems: Orchestration coordinates agents and tools, while multi-agent architectures divide complex responsibilities among specialized agents.

Guardrails & Human-in-the-Loop: Guardrails restrict unsafe or unauthorized behavior, while human approval can remain the final authorization layer for sensitive actions such as trading, treasury operations, or large transfers.

Observability & Evaluation: Production agents need detailed telemetry and systematic evaluation to identify failures in retrieval, tool calls, reasoning, formatting, latency, cost, and safety.

The Bigger Picture

A production AI agent is more than an LLM connected to an API. It combines LLMs, tools, memory, RAG, orchestration, guardrails, observability, and evaluation into a controlled execution system.

These concepts provide the foundation for understanding how modern AI agents are designed and how their individual components work together in real-world applications.

Want to explore each concept in more detail? The full guide breaks down the core AI agent terminology, workflows, architecture, and engineering considerations in greater depth.

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AI Agent Terminology: A Developer’s Guide