AI Agent & Agentic System Vocabulary
5 exercises — Master the vocabulary of autonomous AI systems: ReAct loops, memory types, multi-agent orchestration, reflection, and observability.
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A developer reviews an agent run trace that shows the following repeating pattern:
Thought: The user wants the current weather in London. I should call the weather tool.
Action: get_weather(location="London")
Observation: {"temp": 14, "condition": "cloudy"}
Thought: I now have the data needed to answer.
Final Answer: It's 14 °C and cloudy in London.
What architecture pattern does this trace demonstrate?
ReAct (Reason + Act) is the foundational pattern for tool-using agents.
In the ReAct pattern, the LLM alternates between reasoning ("Thought") and acting ("Action"), then incorporates the tool result ("Observation") into the next reasoning step. This repeating cycle — Thought → Action → Observation → Thought — constitutes the agent loop.
Pure chain-of-thought (B) produces intermediate reasoning steps but makes no tool calls. The planner-executor pattern (D) splits planning and execution into two separate LLM calls. ReAct uses a single LLM that both reasons and acts within the same loop — making it the building block of most single-agent frameworks (LangChain AgentExecutor, LlamaIndex ReActAgent).
Key vocabulary:
• ReAct — Reason + Act; an agent pattern interleaving reasoning steps with tool actions
• thought — the agent's internal reasoning step, visible in the trace
• action — a tool or function call issued by the agent
• observation — the tool's result returned to the agent for the next reasoning step
• agent loop — the repeating Thought → Action → Observation cycle that drives a ReAct agent
In the ReAct pattern, the LLM alternates between reasoning ("Thought") and acting ("Action"), then incorporates the tool result ("Observation") into the next reasoning step. This repeating cycle — Thought → Action → Observation → Thought — constitutes the agent loop.
Pure chain-of-thought (B) produces intermediate reasoning steps but makes no tool calls. The planner-executor pattern (D) splits planning and execution into two separate LLM calls. ReAct uses a single LLM that both reasons and acts within the same loop — making it the building block of most single-agent frameworks (LangChain AgentExecutor, LlamaIndex ReActAgent).
Key vocabulary:
• ReAct — Reason + Act; an agent pattern interleaving reasoning steps with tool actions
• thought — the agent's internal reasoning step, visible in the trace
• action — a tool or function call issued by the agent
• observation — the tool's result returned to the agent for the next reasoning step
• agent loop — the repeating Thought → Action → Observation cycle that drives a ReAct agent