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

AI agents are evolving from simple chat assistants into systems that can reason, plan, use external tools, execute workflows, and complete complex tasks autonomously.

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
   |
   +-- Skills
   |
   +-- Memory
   |
   +-- MCP Integrations

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The model provides intelligence and reasoning. The surrounding components provide the ability to take action.

This article provides a quick tour of the key building blocks behind modern AI agent workflows.

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The AI Agent Workflow

A typical agent execution flow looks like this:

User Request
      |
      v
Agent Harness
      |
      +--> Understand the task
      |
      +--> Load relevant skills
      |
      +--> Retrieve memory
      |
      +--> Select tools
      |
      +--> Execute actions
      |
      +--> Observe results
      |
      +--> Update memory
      |
      v
Task Completed

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The agent continuously follows a cycle:

Reason → Act → Observe → Repeat

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The model decides what should happen next, while the agent infrastructure makes it possible.

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1. Agent Harness: The Runtime Behind the Agent

The AI Agent Harness is the execution layer that manages the lifecycle of an agent.

It coordinates:

• Task execution
• Context management
• Tool access
• Skill loading
• Memory retrieval
• Security policies
• Monitoring

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 service."

The harness handles:

1. Loading customer analytics capabilities
2. Calling the database tool
3. Generating email content
4. Requesting approval if required
5. Sending the email

The harness bridges the gap between reasoning and execution.

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2. Tools: Giving Agents the Ability to Take Action

A tool is a capability that an agent can invoke.

Examples:

• Search engines
• APIs
• Databases
• Code execution environments
• File systems
• Business applications

Without tools, an LLM can only provide recommendations.

With tools, an agent can perform real-world actions.

Example tool definition:

{
  "name": "query_database",
  "description": "Runs SQL queries against customer data",
  "parameters": {
    "query": "string"
  }
}

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The model decides:

"I need customer information.
I will use query_database."

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The harness executes:

SELECT *
FROM customers
WHERE last_login < CURRENT_DATE - INTERVAL '90 days';

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The result is returned to the model, allowing it to continue reasoning.

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3. Model Context Protocol (MCP): Standardizing Agent Connections

As agents become more powerful, they need access to many external systems.

Managing custom integrations for every application becomes difficult.

This is where Model Context Protocol (MCP) helps.

MCP provides a standard way for AI applications to discover and use external tools and data sources.

Without MCP:

Agent
 |
 +-- Custom Database Connector
 +-- Custom File Connector
 +-- Custom API Connector
 +-- Custom Search Connector

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With MCP:

Agent
 |
 +-- MCP Client
        |
        +-- MCP Server: Database
        |
        +-- MCP Server: Files
        |
        +-- MCP Server: Business APIs

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An MCP server can expose capabilities such as:

Available Tools:

- search_customers()
- get_customer_orders()
- update_customer_record()

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The agent can discover these tools dynamically and use them during execution.

MCP creates a clean separation between:

• Agent reasoning
• Tool implementation
• Enterprise system access

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4. Skills: Packaging Reusable Expertise

Tools provide individual actions.

Skills provide reusable workflows and domain knowledge.

A skill represents a higher-level capability that an agent can load when needed.

Examples:

• Software development skill
• Data analysis skill
• Customer support skill
• Security review skill
• Document generation skill

A typical skill package may look like:

customer-support-skill/

├── SKILL.md
├── prompts/
├── workflows/
├── examples/
└── tools/

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The SKILL.md file defines how the agent should use that capability.

Example:

# Customer Support Skill

## Purpose
Resolve customer issues efficiently.

## Workflow

1. Identify customer intent
2. Retrieve account information
3. Review previous interactions
4. Suggest resolution
5. Escalate when required

## Available Tools

- get_customer_profile
- create_ticket
- send_email

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Skills allow agents to become specialized without permanently loading every capability.

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5. Memory: Giving Agents Continuity

LLMs are stateless by default.

Without memory, every interaction starts from zero.

Agent memory usually exists at multiple levels.

Short-Term Memory

Current conversation context.

Example:

User:
"My order arrived damaged."

Agent remembers:

- Order details
- Previous messages
- Current issue

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Working Memory

Temporary information required during a task.

Example:

Research Task:

Files analyzed:
- sales_report.csv
- customer_feedback.json
- product_notes.md

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Long-Term Memory

Information retained across sessions.

Example:

{
  "user_preferences": {
    "communication": "email",
    "language": "English"
  }
}

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Memory allows agents to become more personalized and effective over time.

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A Complete AI Agent Workflow Example

Consider a software engineering agent.

User request:

"Fix the failing payment API tests."

The workflow looks like this:

Step 1: Harness Initializes the Agent

The harness prepares:

• Model
• Context
• Available tools
• Security policies

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Step 2: Load Relevant Skills

The harness loads:

software-engineering-skill/

├── SKILL.md
├── coding-guidelines.md
└── testing-workflow.md

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The agent now understands the expected development process.

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Step 3: Retrieve Memory

The agent retrieves previous context:

Previous changes:

- Payment API migrated recently
- Database schema updated
- Authentication tests were modified

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Step 4: Use Tools Through MCP

The agent connects to:

MCP Servers:

- Git repository
- CI/CD system
- Test runner
- Issue tracker

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Actions:

1. Read failing tests
2. Inspect source code
3. Modify implementation
4. Run test suite
5. Analyze results

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Step 5: Verify and Complete

The harness validates:

• Tests pass
• Changes follow policies
• No restricted actions occurred

The agent provides the final result.

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Why These Components Matter

Building AI agents is no longer only about improving prompts or selecting better models.

Reliable agent systems require:

• Harnesses to manage execution
• Tools to interact with systems
• MCP to standardize integrations
• Skills to package expertise
• Memory to maintain context

The model provides reasoning.

The agent workflow provides the ability to turn reasoning into useful work.

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Final Thoughts

The future of AI applications will be built around agentic workflows rather than standalone chat experiences.

Understanding the relationship between models, harnesses, tools, skills, MCP, and memory is essential for designing reliable AI agents.

The next generation of AI systems will not just answer questions.

They will understand goals, use tools, remember context, execute workflows, and collaborate with humans to complete real-world tasks.