Agent Loop

Agent Memory

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

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.

Plan-and-Execute Pattern

An agentic architecture where a planning module decomposes goals into ordered tasks and a separate executor carries them out, enabling complex multi-step problem solving.

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 capability of AI agents to interact with external tools, APIs, and services to extend their functionality.

Standardised protocols and languages used for inter-agent communication in multi-agent systems.

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.

An AI agent that formulates and pursues explicit goals, planning actions to achieve desired outcomes.

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

The strategic process of redesigning business operations around autonomous AI agents to achieve hyperscale efficiency.

A meta-agent that coordinates, monitors, and manages a team of sub-agents, allocating tasks and synthesising results to fulfil complex multi-domain objectives.

An AI agent that selects actions to maximise a utility function representing the desirability of different outcomes.