ReAct Framework

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.

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 defined role, personality, and behavioural characteristics assigned to an AI agent for consistent interaction.

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.

An autonomous software entity that perceives its environment, makes decisions, and takes actions to achieve specified objectives.

An AI agent that maintains an internal representation of the world to inform its decision-making process.

The transfer of a task or conversation from one specialised AI agent to another based on skill requirements, escalation rules, or domain boundaries.

The sequential composition of multiple AI agents where each agent's output becomes the input for the next, creating automated pipelines for complex multi-stage processes.

The ability to monitor, trace, and understand the internal states, decisions, and actions of AI agents in production.

Language Model

A probabilistic model that assigns probabilities to sequences of words, enabling prediction of the next word in a sequence.