Task Decomposition

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.

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.

A business process that is partially or fully executed by autonomous AI agents rather than human workers.

An organisational structure where agents are arranged in levels, with higher-level agents delegating tasks to lower-level ones.

An AI agent that autonomously navigates web pages, fills forms, extracts information, and completes online tasks by controlling a browser through programmatic or visual interfaces.

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

Complex patterns and capabilities that arise from the interactions of simpler agent components or rules.

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

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.

The process of multiple AI agents working together, sharing information and coordinating actions to achieve common goals.