{"slug": "ai-agentic-workflow-explained-a-quick-tour-of-harness-tools-skills-mcp-and", "title": "AI Agentic Workflow Explained: A Quick Tour of Harness, Tools, Skills, MCP, and Memory", "summary": "A developer explains that production AI agents are composed of multiple components including a model, harness, tools, skills, memory, and MCP integrations. The harness manages the agent lifecycle, tools enable real-world actions, MCP standardizes external tool access, and skills provide reusable workflows. The agent follows a reason-act-observe cycle to complete complex tasks autonomously.", "body_md": "AI agents are evolving from simple chat assistants into systems that can reason, plan, use external tools, execute workflows, and complete complex tasks autonomously.\n\nHowever, an AI agent is not just a Large Language Model (LLM) with a prompt.\n\nA production AI agent is a combination of multiple components working together:\n\n```\nAI Agent\n   |\n   +-- Model\n   |\n   +-- Harness\n   |\n   +-- Tools\n   |\n   +-- Skills\n   |\n   +-- Memory\n   |\n   +-- MCP Integrations\n```\n\nThe model provides intelligence and reasoning. The surrounding components provide the ability to take action.\n\nThis article provides a quick tour of the key building blocks behind modern AI agent workflows.\n\nA typical agent execution flow looks like this:\n\n``` php\nUser Request\n      |\n      v\nAgent Harness\n      |\n      +--> Understand the task\n      |\n      +--> Load relevant skills\n      |\n      +--> Retrieve memory\n      |\n      +--> Select tools\n      |\n      +--> Execute actions\n      |\n      +--> Observe results\n      |\n      +--> Update memory\n      |\n      v\nTask Completed\n```\n\nThe agent continuously follows a cycle:\n\n```\nReason → Act → Observe → Repeat\n```\n\nThe model decides what should happen next, while the agent infrastructure makes it possible.\n\nThe **AI Agent Harness** is the execution layer that manages the lifecycle of an agent.\n\nIt coordinates:\n\nA useful analogy:\n\nFor example:\n\nUser request:\n\n\"Find customers who have not logged in for 90 days and send them a reminder email.\"\n\nThe model reasons:\n\n\"I need customer activity data and an email service.\"\n\nThe harness handles:\n\nThe harness bridges the gap between reasoning and execution.\n\nA tool is a capability that an agent can invoke.\n\nExamples:\n\nWithout tools, an LLM can only provide recommendations.\n\nWith tools, an agent can perform real-world actions.\n\nExample tool definition:\n\n```\n{\n  \"name\": \"query_database\",\n  \"description\": \"Runs SQL queries against customer data\",\n  \"parameters\": {\n    \"query\": \"string\"\n  }\n}\n```\n\nThe model decides:\n\n```\n\"I need customer information.\nI will use query_database.\"\n```\n\nThe harness executes:\n\n```\nSELECT *\nFROM customers\nWHERE last_login < CURRENT_DATE - INTERVAL '90 days';\n```\n\nThe result is returned to the model, allowing it to continue reasoning.\n\nAs agents become more powerful, they need access to many external systems.\n\nManaging custom integrations for every application becomes difficult.\n\nThis is where **Model Context Protocol (MCP)** helps.\n\nMCP provides a standard way for AI applications to discover and use external tools and data sources.\n\nWithout MCP:\n\n```\nAgent\n |\n +-- Custom Database Connector\n +-- Custom File Connector\n +-- Custom API Connector\n +-- Custom Search Connector\n```\n\nWith MCP:\n\n```\nAgent\n |\n +-- MCP Client\n        |\n        +-- MCP Server: Database\n        |\n        +-- MCP Server: Files\n        |\n        +-- MCP Server: Business APIs\n```\n\nAn MCP server can expose capabilities such as:\n\n```\nAvailable Tools:\n\n- search_customers()\n- get_customer_orders()\n- update_customer_record()\n```\n\nThe agent can discover these tools dynamically and use them during execution.\n\nMCP creates a clean separation between:\n\nTools provide individual actions.\n\nSkills provide reusable workflows and domain knowledge.\n\nA skill represents a higher-level capability that an agent can load when needed.\n\nExamples:\n\nA typical skill package may look like:\n\n```\ncustomer-support-skill/\n\n├── SKILL.md\n├── prompts/\n├── workflows/\n├── examples/\n└── tools/\n```\n\nThe `SKILL.md`\n\nfile defines how the agent should use that capability.\n\nExample:\n\n```\n# Customer Support Skill\n\n## Purpose\nResolve customer issues efficiently.\n\n## Workflow\n\n1. Identify customer intent\n2. Retrieve account information\n3. Review previous interactions\n4. Suggest resolution\n5. Escalate when required\n\n## Available Tools\n\n- get_customer_profile\n- create_ticket\n- send_email\n```\n\nSkills allow agents to become specialized without permanently loading every capability.\n\nLLMs are stateless by default.\n\nWithout memory, every interaction starts from zero.\n\nAgent memory usually exists at multiple levels.\n\nCurrent conversation context.\n\nExample:\n\n```\nUser:\n\"My order arrived damaged.\"\n\nAgent remembers:\n\n- Order details\n- Previous messages\n- Current issue\n```\n\nTemporary information required during a task.\n\nExample:\n\n```\nResearch Task:\n\nFiles analyzed:\n- sales_report.csv\n- customer_feedback.json\n- product_notes.md\n```\n\nInformation retained across sessions.\n\nExample:\n\n```\n{\n  \"user_preferences\": {\n    \"communication\": \"email\",\n    \"language\": \"English\"\n  }\n}\n```\n\nMemory allows agents to become more personalized and effective over time.\n\nConsider a software engineering agent.\n\nUser request:\n\n\"Fix the failing payment API tests.\"\n\nThe workflow looks like this:\n\nThe harness prepares:\n\nThe harness loads:\n\n```\nsoftware-engineering-skill/\n\n├── SKILL.md\n├── coding-guidelines.md\n└── testing-workflow.md\n```\n\nThe agent now understands the expected development process.\n\nThe agent retrieves previous context:\n\n```\nPrevious changes:\n\n- Payment API migrated recently\n- Database schema updated\n- Authentication tests were modified\n```\n\nThe agent connects to:\n\n```\nMCP Servers:\n\n- Git repository\n- CI/CD system\n- Test runner\n- Issue tracker\n```\n\nActions:\n\n```\n1. Read failing tests\n2. Inspect source code\n3. Modify implementation\n4. Run test suite\n5. Analyze results\n```\n\nThe harness validates:\n\nThe agent provides the final result.\n\nBuilding AI agents is no longer only about improving prompts or selecting better models.\n\nReliable agent systems require:\n\nThe model provides reasoning.\n\nThe agent workflow provides the ability to turn reasoning into useful work.\n\nThe future of AI applications will be built around agentic workflows rather than standalone chat experiences.\n\nUnderstanding the relationship between models, harnesses, tools, skills, MCP, and memory is essential for designing reliable AI agents.\n\nThe next generation of AI systems will not just answer questions.\n\nThey will understand goals, use tools, remember context, execute workflows, and collaborate with humans to complete real-world tasks.", "url": "https://wpnews.pro/news/ai-agentic-workflow-explained-a-quick-tour-of-harness-tools-skills-mcp-and", "canonical_source": "https://dev.to/naimulkarim/ai-agentic-workflow-explained-a-quick-tour-of-harness-tools-skills-mcp-and-memory-n8g", "published_at": "2026-07-22 04:35:27+00:00", "updated_at": "2026-07-22 04:57:57.640902+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-tools", "ai-infrastructure", "large-language-models"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/ai-agentic-workflow-explained-a-quick-tour-of-harness-tools-skills-mcp-and", "markdown": "https://wpnews.pro/news/ai-agentic-workflow-explained-a-quick-tour-of-harness-tools-skills-mcp-and.md", "text": "https://wpnews.pro/news/ai-agentic-workflow-explained-a-quick-tour-of-harness-tools-skills-mcp-and.txt", "jsonld": "https://wpnews.pro/news/ai-agentic-workflow-explained-a-quick-tour-of-harness-tools-skills-mcp-and.jsonld"}}