Artificial Intelligence(AI) Interview Questions Part 2
Summary
This article explains fundamental AI concepts and distinctions relevant to AI agents, protocols, and workflows, focusing on their interactions and architectures. It covers key integrations like MCP and A2A, AI reasoning paradigms, and differentiates between generative and agentic AI, highlighting autonomous task execution. Salesforce teams working with AI can leverage these insights to design intelligent systems that automate complex tasks, integrate LLMs with external tools effectively, and apply safe AI completion strategies to avoid unwanted outputs.
Takeaways
- Use MCP to connect AI agents to external tools and A2A for inter-agent communication.
- Design multi-agent systems to break complex problems into specialized agent tasks.
- Implement Safe Completion to ensure AI responds safely to dual-use queries.
- Handle null values explicitly in prompts to prevent LLM hallucinations.
- Differentiate between automation, AI workflows, and AI agents for appropriate use.
1. Difference between A2A and MCP At the highest level, the key difference between MCP and A2A lies in what they are designed to connect: MCP (Model Context Protocol) connects an AI agent or Large Language Model (LLM) to external tools and data sources. It is a 22vertical22 integration, providing the AI with the context and functionality it needs to perform a task. A2A (Agent-to-Agent) connects different AI agents to each other. It is a 22horizontal22 integration, enabling a network of specialized agents to collaborate, delegate tasks, and share information. 2. Difference between Agent Registry and Agent Card in A2A Agents can be added to the catalog by providing their Agent Card url which describes the agent22s capabilities, skills and supported authentication schemes. Registry will fetch the content from the provided agent card and index the content to offer search capabilities. 3.