Vocabulary for building AI agents that plan, act, and recover from failure in production systems.
The repeated cycle an agent runs: observe the current state, think about what to do next, take an action, observe the result, repeat — until the goal is met or a stopping condition is hit.
An architecture where multiple specialized agents collaborate on a task, each handling a different sub-problem, with some orchestrating mechanism routing work between them.
A component (often itself an LLM) that manages the overall workflow: deciding which sub-agent or tool to invoke next, passing outputs between steps, and tracking progress toward the goal.
A model of an agent's workflow as a fixed set of states with defined transitions between them, so at any point there's exactly one current state and a known set of next moves.
A smaller agent spun up by a parent process to handle one narrow piece of a larger task, then reports its result back and shuts down.
A structured definition (usually JSON Schema) that describes a tool's name, what it does, and what parameters it accepts. The model reads this to know how and when to call the tool.