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
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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
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At the core of this workflow is a continuous loop:
Reason → Act → Observe → Repeat
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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."
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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.
This creates the agent loop:
Reason → Tool Call → Result → Reason Again
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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
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With MCP:
Agent
|
MCP Client
|
-------------------------
| | |
Database Files APIs
MCP MCP MCP
Server Server Server
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An MCP server can expose capabilities:
search_customers()
get_customer_orders()
update_customer_record()
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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/
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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
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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
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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
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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
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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
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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
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Working Memory
Temporary information needed during execution.
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.
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
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Step 2: Load Relevant Skills
The harness identifies the task and loads:
software-engineering-skill/
├── SKILL.md
├── coding-guidelines.md
└── testing-workflow.md
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Step 3: Retrieve Memory
The agent recalls:
Previous changes:
- Payment API migration completed
- Database schema updated
- Authentication tests modified
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Step 4: Discover Tools Through MCP
The agent accesses:
MCP Servers:
- Git repository
- CI/CD system
- Test runner
- Issue tracker
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Step 5: Execute the Task
Actions:
1. Analyze failing tests
2. Inspect source code
3. Modify implementation
4. Run tests
5. Review results
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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 timeThe 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.
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