Planning

A system that perceives state, selects actions, and pursues goals—often combining LLM reasoning with tools and memory.

Separates planning from execution in agent architectures.

Mechanisms for retaining context across turns/sessions: scratchpads, vector memories, structured stores.

Coordinating tools, models, and steps (retrieval, calls, validation) to deliver reliable end-to-end behavior.

Constraining model outputs into a schema used to call external APIs/tools safely and deterministically.

Forcing predictable formats for downstream systems; reduces parsing errors and supports validation/guardrails.

Attacks that infer whether specific records were in training data, or reconstruct sensitive training examples.

Methods to protect model/data during inference (e.g., trusted execution environments) from operators/attackers.

System design where humans validate or guide model outputs, especially for high-stakes decisions.

Tendency to trust automated suggestions even when incorrect; mitigated by UI design, training, and checks.