Plan-and-Execute Pattern

Agent Memory

The storage mechanism enabling AI agents to retain and recall information from previous interactions and experiences.

Agent Loop

The iterative cycle of perception, reasoning, planning, and action execution that drives autonomous agent behaviour.

ReAct Framework

Reasoning and Acting — a framework where language model agents alternate between reasoning traces and action execution.

Agent Reflection

The ability of an AI agent to evaluate its own outputs and reasoning, identifying errors and improving responses.

Task Decomposition

Breaking down complex tasks into smaller, manageable subtasks that can be distributed among AI agents.

Agent Benchmarking

Standardised evaluation of AI agent capabilities across tasks like tool use, planning, reasoning, and task completion.

Agent Memory Bank

A persistent knowledge store that enables AI agents to accumulate and recall information across sessions, supporting long-term learning and personalised interactions.

Agent Reasoning Loop

The iterative cycle of observation, thought, action, and reflection that AI agents execute to break down complex goals into achievable subtasks and verify progress.

Agentic RAG

An advanced retrieval-augmented generation pattern where an agent dynamically decides what information to retrieve, from which sources, and how to refine queries iteratively.

The degree of independence an AI agent has in making and executing decisions without human approval.

A system where humans monitor AI operations and can intervene when necessary, but don't approve every action.

An AI agent capable of operating independently, making decisions and taking actions without continuous human oversight.

A multi-step business process executed entirely by AI agents with minimal human intervention, spanning planning, execution, monitoring, and error recovery phases.

A system composed of multiple interacting AI agents that collaborate, negotiate, or compete to solve complex problems.

A system design where human oversight and approval are required at critical decision points in automated processes.

A theoretical framework that models the structure and processes of the human mind for building intelligent agents.

The complete set of possible actions available to an AI agent in a given environment, defining the boundaries of what the agent can do to accomplish its objectives.