AI Agentic Workflow Explained: A Quick Tour of Harness, Tools, Skills, MCP, and Memory

AI applications are moving beyond simple chat experiences. The next generation of AI systems are AI agents — systems that can understand goals, reason about problems, use external tools, access enterprise data, and complete multi-step workflows. However, an AI agent is not just a Large Language Model (LLM) with a prompt. A production AI agent is a combination of multiple components working together: AI Agent | +-- Model | +-- Harness | +-- Tools | +-- MCP Integrations | +-- AGENTS.md | +-- Skills | +-- Memory Enter fullscreen mode Exit fullscreen mode The model provides intelligence and reasoning. The surrounding infrastructure provides the ability to take action reliably. Understanding the AI Agent Workflow A typical AI agent execution flow looks like this: User Request | v Agent Harness | +----------------+----------------+ | | v v Load Always-On Context Understand Task | | | v | Select Relevant Skills | | | v +------------------------ Load Skill Context | v Retrieve Memory | v Discover Tools via MCP | v Agent Reasoning | v Execute Actions | v Observe Tool Results | v Update Context/Memory | v Task Completed Enter fullscreen mode Exit fullscreen mode At the core of this workflow is a continuous loop: Reason → Act → Observe → Repeat Enter fullscreen mode Exit fullscreen mode The model decides what should happen next. The agent infrastructure makes those decisions actionable. 1. Agent Harness: The Runtime Behind the Agent The AI Agent Harness is the execution layer that surrounds the model. It manages: Task execution Context assembly Tool access Skill loading Memory retrieval Security policies Observability A useful analogy: The model is the brain. The harness is the environment that allows the brain to interact with the world. For example: User request: "Find customers who have not logged in for 90 days and send them a reminder email." The model reasons: "I need customer activity data and an email capability." The harness handles: Loading relevant capabilities Accessing customer data Generating email content Applying approval rules Sending the communication The harness connects reasoning with execution. 2. Tools: Giving Agents the Ability to Act Tools provide agents with the ability to interact with external systems. Examples: APIs Databases Search engines Code execution environments File systems Business applications Without tools, an LLM can only provide recommendations. With tools, an agent can perform actions. Example: The model decides: "I need customer information. I will query the database." Enter fullscreen mode Exit fullscreen mode The harness executes: SELECT * FROM customers WHERE last_login < CURRENT_DATE - INTERVAL '90 days'; Enter fullscreen mode Exit fullscreen mode The result is returned to the model. This creates the agent loop: Reason → Tool Call → Result → Reason Again Enter fullscreen mode Exit fullscreen mode 3. Model Context Protocol (MCP): Standardizing Tool Connections As agents become more capable, they need access to many external systems. Managing custom integrations for every application does not scale. This is where Model Context Protocol (MCP) becomes important. MCP provides a standard way for AI applications to connect with external tools and data sources. Without MCP: Agent ├── Custom Database Connector ├── Custom File Connector ├── Custom API Connector └── Custom Search Connector Enter fullscreen mode Exit fullscreen mode With MCP: Agent | MCP Client | ------------------------- | | | Database Files APIs MCP MCP MCP Server Server Server Enter fullscreen mode Exit fullscreen mode An MCP server can expose capabilities: search_customers() get_customer_orders() update_customer_record() Enter fullscreen mode Exit fullscreen mode MCP separates: Agent reasoning Tool implementation Enterprise system access 4. AGENTS.md: The Agent's Always-Loaded Instructions As agent systems grow, context management becomes critical. Not every instruction should be loaded for every task. AGENTS.md provides the agent with its always-available operating instructions. It defines the baseline rules for how an agent should work. Typical contents include: Project conventions Coding standards Security rules Repository guidelines Environment instructions Common workflows Example: project/ ├── AGENTS.md ├── src/ ├── tests/ └── skills/ Enter fullscreen mode Exit fullscreen mode Example AGENTS.md: # Agent Instructions ## Project Rules - Follow coding standards - Run tests before committing changes - Never expose secrets - Update documentation when required ## Development Workflow 1. Understand the requirement 2. Inspect existing code 3. Implement changes 4. Validate results 5. Provide a summary Enter fullscreen mode Exit fullscreen mode Because AGENTS.md is always included in the context, it should contain only essential instructions. A large AGENTS.md increases context usage and can reduce agent effectiveness. 5. Skills: Dynamic Agent Capabilities Skills provide specialized knowledge and workflows that are loaded only when relevant. Unlike AGENTS.md, skills are not always included in the context. They are selected dynamically based on the task. Examples: Database migration skill Security review skill Frontend development skill Documentation skill Example structure: skills/ ├── database-migration/ │ └── SKILL.md │ ├── security-review/ │ └── SKILL.md │ └── api-development/ └── SKILL.md Enter fullscreen mode Exit fullscreen mode Example SKILL.md: # Database Migration Skill ## Purpose Safely modify database schemas. ## Workflow 1. Analyze schema changes 2. Create migration scripts 3. Validate compatibility 4. Run migration tests 5. Review rollback strategy Enter fullscreen mode Exit fullscreen mode The harness loads this skill only when a database migration task appears. AGENTS.md vs Skills: Why Both Matter A common mistake is putting every instruction into AGENTS.md. Example: AGENTS.md - Coding rules - Database procedures - Deployment workflows - Security reviews - Testing strategies - Documentation rules Enter fullscreen mode Exit fullscreen mode Over time, this file becomes too large. The agent receives unnecessary information for every task. A better approach: AGENTS.md Always loaded: - Core rules - Project conventions - Security requirements Skills Loaded when needed: - Database workflows - Deployment procedures - Security analysis - API patterns Enter fullscreen mode Exit fullscreen mode The goal is not maximum information. The goal is the right information at the right time. 6. Memory: Giving Agents Continuity LLMs are stateless by default. Without memory, every interaction starts from zero. Agent memory usually has multiple layers. Short-Term Memory Current conversation context. Example: User: "My order arrived damaged." Agent remembers: - Order details - Previous messages - Current issue Enter fullscreen mode Exit fullscreen mode Working Memory Temporary information needed during execution. Example: Research Task: Files analyzed: - sales_report.csv - customer_feedback.json - product_notes.md Enter fullscreen mode Exit fullscreen mode Long-Term Memory Information retained across sessions. Example: { "user_preferences": { "communication": "email", "language": "English" } } Enter fullscreen mode Exit fullscreen mode Memory allows agents to become more personalized and effective. Complete AI Agent Workflow Example Consider a software engineering agent. User request: "Fix the failing payment API tests." Step 1: Load Agent Instructions The harness loads: AGENTS.md - Coding standards - Repository rules - Security policies Enter fullscreen mode Exit fullscreen mode Step 2: Load Relevant Skills The harness identifies the task and loads: software-engineering-skill/ ├── SKILL.md ├── coding-guidelines.md └── testing-workflow.md Enter fullscreen mode Exit fullscreen mode Step 3: Retrieve Memory The agent recalls: Previous changes: - Payment API migration completed - Database schema updated - Authentication tests modified Enter fullscreen mode Exit fullscreen mode Step 4: Discover Tools Through MCP The agent accesses: MCP Servers: - Git repository - CI/CD system - Test runner - Issue tracker Enter fullscreen mode Exit fullscreen mode Step 5: Execute the Task Actions: 1. Analyze failing tests 2. Inspect source code 3. Modify implementation 4. Run tests 5. Review results Enter fullscreen mode Exit fullscreen mode Step 6: Verify and Complete The harness validates: Tests pass Policies are followed No restricted actions occurred The agent delivers the result. Why Agentic Workflows Matter Building AI agents is no longer only about choosing a powerful model. Reliable agents require: Component Purpose Harness Runs and coordinates the agent Tools Enable external actions MCP Standardizes integrations AGENTS.md Provides always-on instructions Skills Provide task-specific expertise Memory Maintains context over time The model provides intelligence. The workflow around the model provides capability. Final Thoughts The future of AI applications will be built around agentic workflows. Successful AI agents will combine: Reasoning models Execution harnesses Tool ecosystems MCP-based integrations Dynamic skills Context-aware memory The biggest shift is moving from: "How do we prompt the model?" to: "How do we design the complete system around the model?" That is the foundation of modern AI agent engineering. Enter fullscreen mode Exit fullscreen mode