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
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.
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
The agent continuously follows a cycle:
Reason → Act → Observe → Repeat
The model decides what should happen next, while the agent infrastructure makes it possible.
The AI Agent Harness is the execution layer that manages the lifecycle of an agent.
It coordinates:
A useful analogy:
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:
The harness bridges the gap between reasoning and execution.
A tool is a capability that an agent can invoke.
Examples:
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"
}
}
The model decides:
"I need customer information.
I will use query_database."
The harness executes:
SELECT *
FROM customers
WHERE last_login < CURRENT_DATE - INTERVAL '90 days';
The result is returned to the model, allowing it to continue reasoning.
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
With MCP:
Agent
|
+-- MCP Client
|
+-- MCP Server: Database
|
+-- MCP Server: Files
|
+-- MCP Server: Business APIs
An MCP server can expose capabilities such as:
Available Tools:
- search_customers()
- get_customer_orders()
- update_customer_record()
The agent can discover these tools dynamically and use them during execution.
MCP creates a clean separation between:
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:
A typical skill package may look like:
customer-support-skill/
├── SKILL.md
├── prompts/
├── workflows/
├── examples/
└── tools/
The SKILL.md
file defines how the agent should use that capability.
Example:
## 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
Skills allow agents to become specialized without permanently every capability.
LLMs are stateless by default.
Without memory, every interaction starts from zero.
Agent memory usually exists at multiple levels.
Current conversation context.
Example:
User:
"My order arrived damaged."
Agent remembers:
- Order details
- Previous messages
- Current issue
Temporary information required during a task.
Example:
Research Task:
Files analyzed:
- sales_report.csv
- customer_feedback.json
- product_notes.md
Information retained across sessions.
Example:
{
"user_preferences": {
"communication": "email",
"language": "English"
}
}
Memory allows agents to become more personalized and effective over time.
Consider a software engineering agent.
User request:
"Fix the failing payment API tests."
The workflow looks like this:
The harness prepares:
The harness loads:
software-engineering-skill/
├── SKILL.md
├── coding-guidelines.md
└── testing-workflow.md
The agent now understands the expected development process.
The agent retrieves previous context:
Previous changes:
- Payment API migrated recently
- Database schema updated
- Authentication tests were modified
The agent connects to:
MCP Servers:
- Git repository
- CI/CD system
- Test runner
- Issue tracker
Actions:
1. Read failing tests
2. Inspect source code
3. Modify implementation
4. Run test suite
5. Analyze results
The harness validates:
The agent provides the final result.
Building AI agents is no longer only about improving prompts or selecting better models.
Reliable agent systems require:
The model provides reasoning.
The agent workflow provides the ability to turn reasoning into useful work.
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.