Agentic RAG

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.

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.

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

AI systems that can autonomously plan, reason, and take actions to achieve goals with minimal human intervention.

The coordination and management of multiple AI agents working together to accomplish complex workflows.

An isolated environment where AI agents can safely execute actions and experiment without affecting production systems.

A mechanism allowing language models to invoke external functions or APIs based on natural language instructions.

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

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 interprets datasets, generates visualisations, performs statistical analysis, and produces actionable insights through autonomous exploratory data investigation.

Retrieval-Augmented Generation

A technique combining information retrieval with text generation, allowing AI to access external knowledge before generating responses.