Agent Memory

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.

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.

Methods and metrics for assessing the performance, reliability, and safety of autonomous AI agents.

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

An AI agent that interacts with graphical user interfaces by perceiving screen content and executing mouse clicks, keyboard inputs, and navigation actions like a human operator.

The processes of developing, deploying, monitoring, updating, and retiring AI agents throughout their operational life.

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

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

A multi-agent scenario where agents pursue conflicting objectives, leading to adversarial or game-theoretic interactions.

Recall

The ratio of true positive predictions to all actual positive instances, measuring completeness of positive identification.