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.
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
The agent continuously follows a cycle:
Reason → Act → Observe → Repeat
The model decides what should happen next, while the agent infrastructure makes it possible.
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:
- Loading customer analytics capabilities
- Calling the database tool
- Generating email content
- Requesting approval if required
- Sending the email
The harness bridges the gap between reasoning and execution.
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"
}
}
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.
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
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:
- Agent reasoning
- Tool implementation
- Enterprise system access
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/
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
Skills allow agents to become specialized without permanently loading every capability.
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
Working Memory
Temporary information required during a task.
Example:
Research Task:
Files analyzed:
- sales_report.csv
- customer_feedback.json
- product_notes.md
Long-Term Memory
Information retained across sessions.
Example:
{
"user_preferences": {
"communication": "email",
"language": "English"
}
}
Memory allows agents to become more personalized and effective over time.
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
Step 2: Load Relevant Skills
The harness loads:
software-engineering-skill/
├── SKILL.md
├── coding-guidelines.md
└── testing-workflow.md
The agent now understands the expected development process.
Step 3: Retrieve Memory
The agent retrieves previous context:
Previous changes:
- Payment API migrated recently
- Database schema updated
- Authentication tests were modified
Step 4: Use Tools Through MCP
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
Step 5: Verify and Complete
The harness validates:
- Tests pass
- Changes follow policies
- No restricted actions occurred
The agent provides the final result.
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.
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.
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