AI Agent Protocol Guide
A developer's guide to the essential protocols and technologies powering autonomous AI agents
If you’re building AI agents or trying to understand the ecosystem, you’ve probably encountered a maze of protocols, patterns, and technologies. This glossary breaks down the key concepts you’ll need, arranged so each builds on the last.
Embeddings
Embeddings are numerical vectors that capture semantic meaning turning words and concepts into lists of numbers that position similar ideas closer together in mathematical space. This enables similarity search and semantic understanding: a query for “increase revenue” can find documents about “sales growth” even without matching keywords. A typical use case is semantic search where user queries become vectors, compared against document vectors using (cosine) similarity to find meaning based matches.
Practical context: Mature technology, widely deployed.
Reference: OpenAI’s Embeddings Guide
Vector Databases
Vector Databases are specialized storage systems for embedding vectors, optimized for similarity search rather than exact matches finding “nearest neighbors” among millions of vectors in milliseconds. A typical use case is a recommendation system finding similar users or products, or enabling RAG by storing document chunks as searchable vectors.
Builds on: Embeddings
Practical context: Production ready. Popular options: pgvector, SQLite-Vector, Pinecone, Weaviate, Qdrant, Chroma, LanceDB
Reference: Pinecone’s Vector Database Guide
Retrieval Augmented Generation (RAG)
RAG enhances LLMs by retrieving relevant information at query time converting questions to embeddings, searching vector databases for context, then injecting that context into prompts for grounded, accurate responses. A typical use case is an enterprise chatbot answering questions about internal docs by retrieving relevant sections and generating responses with citations.
Builds on: Embeddings + Vector Databases
Practical context: Industry standard for grounding LLMs. Reduces hallucinations significantly.
Reference: LangChain’s RAG Tutorial
Guardrails
Guardrails validate and control LLM inputs/outputs through rules, filters, and monitors ensuring safe, accurate, compliant responses by checking structure, blocking harmful content, and enforcing boundaries. A typical use case is a healthcare bot that blocks medical advice beyond its scope, validates HIPAA compliance, and verifies drug dosages against databases.
Practical context: Essential for production deployments. Multiple frameworks available.
Reference: Guardrails AI Documentation
Agent
An Agent is LLM driven software that perceives, reasons, and acts autonomously breaking down problems, using tools, maintaining context, and adapting to unexpected situations unlike scripted bots. A typical use case is a customer service agent that handles tickets by understanding complaints, querying databases, processing refunds, and sending updates autonomously.
Enabled by: All the above (RAG for knowledge, Guardrails for safety)
Practical context: Rapidly evolving field. Most use ReAct or similar patterns.
Reference: Anthropic’s Building Effective Agents
Model Context Protocol (MCP)
MCP is Anthropic’s open standard for connecting LLMs to external data sources and tools through a unified client server architecture one protocol instead of countless custom integrations. A typical use case is a coding assistant accessing GitHub repos, databases, documentation, and sandboxes simultaneously through a single standardized interface.
Enables: Agents to access external resources
Practical context: New but gaining adoption. Growing ecosystem of MCP servers.
Reference: Model Context Protocol Documentation
Agent User Interaction (AG-UI)
AG-UI provides standardized components for agent human interfaces handling chat, voice, visualizations, and action confirmations while maintaining conversation context. A typical use case is a data analyst interface showing queries, SQL generation, interactive charts, and voice commands through pre built components.
Complements: MCP (backend) with frontend components
Practical context: Framework for building consistent agent UIs.
Reference: AG-UI Documentation
Agent Communication Protocol (ACP)
ACP by IBM Research standardizes how agents discover, authenticate, and message each other defining performatives (request, inform, propose), conversation patterns, and negotiation protocols. A typical use case is supply chain agents negotiating prices, coordinating with inventory systems, and finalizing orders through structured protocols.
Enables: Structured agent-to-agent communication
Contrast with A2A: Internal, structured agent communication within a platform
Practical context: Enterprise focused, formal specification approach.
Reference: IBM Agent Communication Protocol
Agent2Agent (A2A)
A2A enables decentralized peer-to-peer agent communication using cryptographic signatures and distributed discovery no central coordinator, with identity verification and reputation tracking. A typical use case is a decentralized AI marketplace where agents discover services, negotiate terms, establish smart contracts, and build reputation scores.
Contrast with ACP: Cross-platform, cross-vendor agent interoperability
Practical context: Emerging standard for Web3 style agent networks.
Reference: Agent2Agent Protocol
Agent Network Protocol (ANP)
ANP orchestrates large scale multi-agent systems with routing, load balancing, and consensus mechanisms managing hundreds or thousands of collaborating agents with fault tolerance. A typical use case is a financial analysis network decomposing research into parallel subtasks across specialized agent clusters, handling failures through automatic redistribution.
Orchestrates: Multiple agents using ACP or A2A for communication
Practical context: For complex multi-agent coordination at scale.
Reference: Agent Network Protocol
Agent Payments Protocol (AP2)
AP2 enables autonomous economic transactions between agents micropayments, escrow, metered billing allowing agents to buy/sell resources and services without human intervention. A typical use case is an AI research agent purchasing GPU time, buying datasets, and selling trained models while managing budgets and maintaining audit records.
Builds on: Agent communication protocols + payment rails
Practical context: Early stage. Bridges traditional payments and crypto.
Reference: Agent Payments Protocol
Conclusion
What do you think? Did I miss something critical? Is there something you’d like to know more about? Let me know.