Agentic

Interaction Protocols

Overview

Protocols

Aspect	Function Calling	Model Context Protocol (MCP)	Agent-2-Agent (A2A)
Visualization
Purpose	Allows Large Language Models (LLMs) to interact with external tools, APIs, or databases by generating structured function calls in response to user prompts. Extends the LLM's capabilities beyond its training data	Standardizes how AI models (LLMs) integrate and share data with external tools, systems, and data sources. Connects AI to the "real world" context	Enables AI agents to communicate and collaborate securely across platforms and vendors. Creates a "universal language" for agents to work together
Integration Paradigm	Direct integration: LLM to external functions/APIs. Extends the LLM's immediate action capabilities	Vertical integration: AI model to external data/tools. Extends AI's context awareness	Horizontal integration: Agent to Agent communication. Enables multi-agent workflows
Core Entities Interacting	LLM: generates function calls External Tool/API: executes the function call	Host: AI app Client: intermediary Server: data/tool provider	Client Agent: requests tasks Remote Agent: performs tasks
Key Abstractions	Function Definitions: schema describing available functions (name, parameters, description) Function Calls: structured output from LLM (function name, arguments)	Tools: executable functions Resources: structured data streams Prompts: instruction templates	Agent Cards: capability discovery Tasks: unit of work Parts: multi-modal content
Communication Protocol	Varies by LLM provider, often JSON-based, embedded within the LLM's response format	JSON-RPC 2.0 over stateful connections. Inspired by Language Server Protocol (LSP)	JSON-RPC 2.0 for message exchange; HTTP(S) as transport; Server-Sent Events (SSE) for real-time streaming
Content Types Supported	Typically structured data (JSON) for function arguments and tool output; can be text, and potentially other modalities depending on the tool	Structured data streams (Resources), API responses, file contents, logs	Multi-modal "Parts": TextPart, FilePart (binary data), DataPart (structured JSON)
Capability Discovery Mechanism	Function definitions provided to the LLM at the time of the API call, often via a prompt or a dedicated tools parameter	Dynamic tool discovery: AI queries for available tools at runtime	Agent Cards: Machine-readable manifests describing agent skills, I/O types, authentication
Task Management Model	Single-shot or chained execution of functions as determined by the LLM in response to a user query. Typically, a request-response model	Focus on providing context and tools for AI to execute tasks. AI decides tool use	Structured around "Tasks" with unique IDs and defined states; supports long-running tasks with progress updates
Security & Authentication Approach	Leverages existing security mechanisms of the external APIs being called. Responsibility for secure execution typically lies with the application integrating the LLM and the tools	User consent and control, data privacy, tool safety. Requires explicit user consent for data access/operations/tool invocation	"Secure by Default." Standardized access controls, authentication/authorization options (e.g., JWTs for push notifications). "Opaque" agent design
Pros	Extends LLM capabilities, simple to implement for basic interactions, allows LLMs to access real-time information and perform actions	Standardized integration, enhanced context awareness, dynamic tool discovery, improved security/access control, ecosystem growth	Cross-platform communication, scalability without rework, smarter automation, faster time-to-value, unified governance
Cons	Hallucination (LLM might invent functions or arguments), security risks if not properly sandboxed, limited context for complex multi-step processes, vendor-specific implementations	Engineering complexity, scalability/performance, potential fragmentation, identity management, identified security vulnerabilities (prompt injection, tool permissions)	Inherited complexity/cost of multi-agent systems. Ongoing development for advanced features (e.g., dynamic UX negotiation)
Relationship to LLMs/Agents	A fundamental capability that allows LLMs to interact with the external world and perform actions beyond generating text	Grounds LLMs/agents in real-time, external data and enables them to take actions in the real world	Enables communication between autonomous AI agents, regardless of their internal LLM or framework
Use Cases	Chatbots retrieving real-time data (weather, stock prices), executing simple commands (sending emails, setting reminders), data retrieval from databases	Enterprise assistants (CRM, docs), natural language data access (SQL), desktop assistants (file access), multi-tool agents, customer support chatbots, personalized learning, healthcare diagnostics	Enterprise automation (ordering, supply chain), hiring process simplification, customer experience, general multi-agent orchestration

Design Patterns

Pattern	Visualization	Definition	Use Cases
Prompt Chaining		decomposes complex LLM tasks into sequential sub-problems, improving reliability by focusing each step and using structured outputs like JSON. It overcomes single-prompt limitations such as errors and hallucinations, enabling AI agents for multi-step reasoning and external tool integration	Information Processing Workflows; Complex Query Answering; Data Extraction and Transformation; Content Generation Workflows; Conversational Agents with State; Code Generation and Refinement; Multimodal and multi-step reasoning
Routing		enables dynamic decision-making in agentic systems, shifting from fixed sequential workflows to adaptive paths based on inputs like user intent or state. Mechanisms: LLM-based (prompt for classification), Embedding-based (semantic similarity), Rule-based (logic on keywords), ML Model-based (fine-tuned classifier)	Chain summarization; Trend analysis; Email drafting for market reports
Parallelization		enables concurrent execution of independent sub-tasks (e.g., LLM calls, tool usages, or sub-agents) in agentic workflows, unlike sequential Prompt Chaining or dynamic Routing. This reduces overall time by running tasks simultaneously, especially with external latencies	Information Gathering and Research; Data Processing and Analysis; Multi-API or Tool Interaction; Content Generation with Multiple Components; Validation and Verification; Multi-Modal Processing; A/B Testing or Multiple Options Generation
Reflection		enables AI agents to evaluate and refine their own outputs or processes, improving accuracy, completeness, and quality beyond basic patterns like Chaining, Routing, or Parallelization. It introduces a feedback loop where an agent critiques its work and iterates for better results	Creative Writing and Content Generation; Code Generation and Debugging; Complex Problem Solving; Summarization and Information Synthesis; Planning and Strategy; Conversational Agents
Tool Use (Function Calling)		enables agents to interact with external systems (APIs, databases, services, or code) via Function Calling. It allows LLMs to decide when and how to invoke tools based on user requests	Information Retrieval from External Sources; Interacting with Databases and APIs; Performing Calculations and Data Analysis; Sending Communications; Executing Code; Controlling Other Systems or Devices
Planning		enables an AI agent to autonomously create action sequences from an initial state to a goal state, adapting to new information and obstacles	Procedural Task Automation; Robotics and Autonomous Navigation; Structured Information Synthesis; Customer Support
Multi-Agent Collaboration		overcomes limitations of monolithic agents for complex, multi-domain tasks by decomposing objectives into sub-problems assigned to specialized agents (e.g., a Research Agent for retrieval, Data Analysis for processing, and Synthesis for reporting). Success relies on standardized communication protocols and shared ontologies for data exchange, delegation, and coordination, ensuring coherent outputs. Models include: single agent, network, supervisor, supervisor as a tool, hierarchical, and custom	Coordinated Research Efforts; Collaborative Data Analysis; Joint Content Creation; Multi-Agent Planning and Strategy
Memory Management		agents need memory like humans: short-term (contextual, in LLM windows; limited, ephemeral) and long-term (persistent, in databases; semantic retrieval for integration)	Chatbots and Conversational AI; Task-Oriented Agents; Personalized Experiences; Learning and Improvement; Information Retrieval (RAG); Autonomous Systems
Learning and Adaptation		AI agents improve autonomously through various learning types including reinforcement learning with reward systems, supervised learning using labeled data, unsupervised learning for pattern discovery, few-shot/zero-shot adaptation, online real-time updates, and memory-based contextual learning to handle novel situations. Key algorithms like Proximal Policy Optimization (PPO) provide stable reinforcement learning for continuous actions, while Direct Preference Optimization (DPO) offers a simpler approach to align large language models directly with human preferences without requiring separate reward models	Personalized assistant agents; Trading bot agents; Application agents; Robotic and autonomous vehicle agents; Fraud detection agents
Model Context Protocol		open standard enabling LLMs to interact with external tools, data, and systems through a client-server architecture, acting as a universal adapter for dynamic discovery and interoperability. It exposes resources (static data), tools (executable actions), and prompts (interaction templates), promoting reusability and composability while wrapping legacy systems without rewrites. Unlike proprietary tool function calling, MCP supports broad, standardized connections, with considerations for security, error handling, and agent-friendly data formats. The interaction flow involves discovery, request formulation, server execution, and response updates to enable advanced agentic behavior	Database Integration; Generative Media Orchestration; External API Interaction; Reasoning-Based Information Extraction; Custom Tool Development; Standardized LLM-to-Application Communication; Complex Workflow Orchestration; IoT Device Control; Financial Services Automation
Goal Setting and Monitoring		AI agents need defined objectives and progress tracking to succeed. This pattern sets goals, monitors advancement, and verifies success	Customer Support Automation; Personalized Learning Systems; Project Management Assistants; Automated Trading Bots; Robotics and Autonomous Vehicles; Content Moderation
Exception Handling and Recovery		enables AI agents to detect and recover from errors, malfunctions, and challenges using strategies like logging, retries, fallbacks, degradation, notifications, rollback, diagnosis, self-correction, and escalation, promoting resilience in unpredictable environments	Customer Service Chatbots; Automated Financial Trading; Smart Home Automation; Data Processing Agents; Web Scraping Agents; Robotics and Manufacturing
Human-in-the-Loop		integrates human judgment with AI to ensure ethical, safe deployment in complex domains, augmenting human capabilities through oversight, intervention, feedback, and collaboration. It enables AI use in sensitive sectors but faces scalability, expertise, and privacy challenges	Content Moderation; Autonomous Driving; Financial Fraud Detection; Legal Document Review; Customer Support (Complex Queries); Data Labeling and Annotation; Generative AI Refinement; Autonomous Networks
Knowledge Retrieval (RAG)		integrates external knowledge bases into LLMs for accurate, up-to-date responses. It uses semantic search to retrieve relevant chunks, augments prompts, and generates grounded outputs, reducing hallucinations. Core elements: embeddings (text vectors), similarity (semantic closeness), chunking (document breakdown), vector databases (efficient embedding storage/query). Variants: Graph RAG (knowledge graphs for complex queries); Agentic RAG (reasoning agent for validation, reconciliation, multi-step synthesis). Challenges: fragmented info, retrieval quality, maintenance costs, adds latency/complexity	Enterprise Search and Q&A; Customer Support and Helpdesks; Personalized Content Recommendation; News and Current Events Summarization
Inter-Agent Communication (A2A)		A2A is an open protocol for AI agents from different frameworks to collaborate via tasks, messages, and artifacts over JSON-RPC 2.0. Key features include Agent Cards for discovery, interaction modes (synchronous, polling, streaming, push), and security measures like TLS. Supported by major companies, it enhances multi-agent AI efficiency and complements MCP	Multi-Framework Collaboration; Automated Workflow Orchestration; Dynamic Information Retrieval
Resource-Aware Optimization		allows agents to dynamically allocate computational, temporal, and financial resources to meet goals within budgets or maximize efficiency. Agents choose between accurate/costly models and fast/cheap ones, such as quick summaries for limited resources or detailed forecasts for ample time/budget. A fallback switches to affordable models if needed, ensuring continuity	Cost-Optimized LLM Usage; Latency-Sensitive Operations; Energy Efficiency; Fallback for service reliability; Data Usage Management; Adaptive Task Allocation
Reasoning Techniques		reasoning techniques for AI agents, including multi-step inferences, problem decomposition, and increased inference resources for better accuracy. Key methods include Chain-of-Thought (CoT) for step-by-step logic, Tree-of-Thought (ToT) for branching paths, Self-Correction for refinement, ReAct for tool-integrated actions, and collaborative frameworks like Chain of Debates (CoD) and Multi-Agent System Search (MASS). The Scaling Inference Law enables smaller models to outperform larger ones with extended "thinking time," as seen in applications like Deep Research, fostering transparent, autonomous agents for complex tasks	Complex Question Answering; Mathematical Problem Solving; Code Debugging and Generation; Strategic Planning; Medical Diagnosis; Legal Analysis
Guardrails/Safety		ensure AI agents avoid harmful outputs via input validation, output filtering, behavioral constraints, and oversight. Examples: CrewAI uses prompts and Pydantic models; Vertex AI employs validation callbacks. Reliable agents need modularity, observability, least privilege, and fault-tolerant patterns like checkpoints for robustness	Customer Service Chatbots; Content Generation Systems; Educational Tutors/Assistants; Legal Research Assistants; Recruitment and HR Tools; Social Media Content Moderation; Scientific Research Assistants
Evaluation and Monitoring		enables AI agents to continuously assess performance, track progress toward goals, and detect operational anomalies through metrics, feedback loops, and reporting systems, ensuring alignment with requirements in dynamic environments. It incorporates response accuracy, latency monitoring, token usage tracking, LLM-as-a-Judge evaluations, agent trajectories, testing via files and evalsets, multi-agent dynamics, and advanced frameworks like "contractors" with formalized pillars for reliable, production-ready operation	Performance Tracking in Live Systems; A/B Testing for Agent Improvements; Compliance and Safety Audits; Enterprise systems; Drift Detection; Anomaly Detection in Agent Behavior; Learning Progress Assessment
Prioritization		helps AI agents in complex settings rank tasks by urgency, importance, dependencies, and resources, focusing on critical actions for efficiency and goal alignment. It includes defining criteria, evaluating tasks, selecting actions via algorithms, and re-prioritizing dynamically, applicable at goal, sub-task, or action levels	Automated Customer Support; Cloud Computing; Autonomous Driving Systems; Financial Trading; Project Management; Cybersecurity; Personal Assistant AIs
Exploration and Discovery		enables AI agents to proactively explore novel information in complex environments, moving beyond reactive optimization	Scientific Research Automation; Game Playing and Strategy Generation; Market Research and Trend Spotting; Security Vulnerability Discovery; Creative Content Generation; Personalized Education and Training