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Agents in the HAPI Stack

Agents are the reasoning engines at the heart of the HAPI Stack, orchestrating context-driven conversational flows. They coordinate user input, context management, tool invocation, and memory, leveraging the Model Context Protocol (MCP) and API contracts for robust, API-first interactions.

Agent Architecture Overview​

HAPIAgentArchitectureThe Happiness StackAgent CoreReasoning EngineInput Processing📥 Prompts🔤 NLP🧠 Intent🔍 ContextKnowledge Retrieval🔍 RAG📚 LLM KB🌐 Internet🔄 UpdateActions🧩 Tools🔧 Functions🌐 API📋 PlansMemory🗄️ Database⚡ Cache📚 Long Term💾 Short TermOutput Generation🔤 Response📊 Format📝 Refine🖼️ MediaUser Interface💬 Chat🖥️ Web📱 Mobile🔌 APIProcesses user inputs andextracts intents &entitiesRetrieves relevant informationfrom various knowledge sourcesExecutes actions andinterfaces with external systemsStores and retrieves informationwith different persistence levelsCoordinates all componentsand manages the reasoning processHandles interactions with usersacross multiple platformsGenerates and refines responsesin appropriate formatsMetricsHAPIEcosystem

The HAPI components diagram illustrates the high-level architecture where agents operate:

  • Agent Core: Central reasoning engine that coordinates all modules and manages the flow of information.
  • Input Processing: Handles user prompts, natural language processing (NLP), intent extraction, and context parsing.
  • Knowledge Retrieval: Uses RAG (Retrieval-Augmented Generation), LLM knowledge bases, and internet sources to fetch relevant information.
  • Actions: Executes structured actions via MCP tools, functions, APIs, and planning modules.
  • Memory: Manages short-term (chat history) and long-term (database, cache) memory for continuity and personalization.
  • Output Generation: Produces, refines, and formats responses, including media generation.
  • User Interface: Connects agents to chat, web, mobile, and API endpoints for seamless user interaction.

How Agents Orchestrate Chat Flows​

Agents in the HAPI Stack implement Context Engineering (see ChatMCP Context Engineering) to ensure every chat flow is:

  • Context-Driven: Each message is processed with full awareness of session and context, as defined by MCP.
  • API-First: All actions and tool calls are validated against OpenAPI contracts.
  • Composable: Agents support direct, group, and mediated conversations.
  • Memory-Driven: Persistent and ephemeral memory enables continuity and personalization.

Flow Example​

  1. User Input: The agent receives a message, which is added to the chat history.
  2. Prompt Construction: The agent builds a prompt from recent messages and context.
  3. Reasoning: The agent processes the prompt, may invoke RAG or tools.
  4. Tool Invocation: Structured actions are performed via OpenAPI tools (e.g., fetch weather).
  5. Answer Generation: The agent generates a response, which is returned to the user and stored in memory.

Integration with MCP and OpenAPI​

  • MCP (Model Context Protocol): Defines context schema, memory management, and agent orchestration.
  • OpenAPI: Specifies all agent actions and tool invocations, ensuring contract-driven flows.

Agents validate all tool inputs/outputs against OpenAPI schemas, preventing runtime errors and enabling dynamic, context-aware interactions.

Best Practices​

  • Design agent actions with OpenAPI first.
  • Persist context for smarter, personalized flows.
  • Leverage memory modules for continuity.
  • Test agent flows with real users and scenarios.

Further Reading​