Agent Benchmarking

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

A catalogue of available tools and APIs that agents can discover and invoke, with descriptions, schemas, and authentication details enabling dynamic capability acquisition.

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

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

A workflow pattern where multiple specialised agents are sequentially connected, with each agent's output feeding the next.

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

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

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