Questions et reponses d'entretien

An AI Agent is an autonomous or semi-autonomous software entity that perceives its environment through inputs (sensors or data sources), processes information using reasoning or learning algorithms, and takes actions through actuators or system operations to achieve specific goals. AI agents can operate continuously, adapt to changing environments, learn from experience, and optimize decision-making over time. Modern AI agents often combine Large Language Models (LLMs), planning mechanisms, memory systems, and external tools to perform complex tasks with minimal human intervention.

Example:

A customer service AI agent that reads user queries, searches internal documentation, generates responses, escalates complex issues, and learns from past interactions.

An AI Agent architecture typically consists of several key components: (1) Perception Layer to gather data from users or systems, (2) Reasoning or Decision Engine to analyze information, (3) Memory to store context, history, or learned knowledge, (4) Planning Module to determine sequences of actions, (5) Learning Component for improving performance using feedback or data, and (6) Action/Execution Layer to interact with external tools, APIs, or environments. Modern agents also include tool orchestration and feedback loops.

Example:

An AI coding assistant perceives user prompts, reasons about the programming problem, recalls past context, plans steps to generate code, executes via code generation tools, and refines answers using feedback.

AI agents operate based on defined goals. Complex goals are divided into smaller subgoals that can be solved sequentially or in parallel. Subgoal management helps agents track progress, prioritize tasks, and handle dependencies effectively.

Example:

Goal: Launch a product. Subgoals: market research, competitor analysis, pricing strategy, and marketing campaign creation.

An agent knowledge base stores structured and unstructured information used for reasoning and decision-making. It may include documents, rules, embeddings, ontologies, and historical experiences. Knowledge bases enable domain-specific intelligence.

Example:

A healthcare AI agent accessing medical guidelines stored in its knowledge base.

Agent personalization adapts AI behavior based on individual user preferences, history, roles, or context. Personalization improves user experience and task efficiency.

Example:

A learning assistant adapting explanations based on a student's skill level.