How to Build Agentic AI

What Makes AI "Agentic"?

An agentic AI system is one that can take independent action to achieve goals, rather than simply responding to prompts. The key distinction from traditional LLM applications is the autonomy loop: the ability to perceive, reason, plan, act, and learn—continuously.

Five Defining Characteristics

Core Architecture Components

A functional agentic AI system comprises several interconnected modules that work together to create autonomous behavior. According to Exabeam's architecture guide, these components mimic a cognitive process.

Source: Exabeam: Agentic AI Architecture, Kore.ai Architecture Blueprint

Framework Selection Guide

Choosing the right framework is one of the most consequential early decisions. The wrong choice can mean costly rewrites 6–12 months in. Here's how the major frameworks compare for different use cases, based on DataCamp's technical comparison.

For a deeper comparison, see our Best Agentic AI Frameworks in 2026 guide.

Tool Integration with Model Context Protocol (MCP)

The Model Context Protocol (MCP) has emerged as the industry standard for connecting AI agents to external tools and data sources. Introduced by Anthropic in November 2024 and adopted by OpenAI in March 2025, MCP is now the de facto standard for agent-tool communication.

How MCP Works

from mcp.server import Server
from mcp.types import Tool, TextContent

app = Server("my-tools")

@app.tool()
async def search_database(query: str) -> str:
    """Search the company database for information.

    Args:
        query: The search query to execute
    """
    # Execute search against your database
    results = await db.search(query)
    return format_results(results)

@app.tool()
async def send_notification(
    channel: str,
    message: str
) -> str:
    """Send a notification to a Slack channel.

    Args:
        channel: The Slack channel name
        message: The message content to send
    """
    await slack_client.post_message(channel, message)
    return f"Sent to #{channel}"

# Run the server
if __name__ == "__main__":
    app.run()

Key Best Practices for Tool Integration

Sources: Model Context Protocol, Anthropic Announcement, OpenAI MCP Integration

Prompt Engineering for Agents

Prompt engineering for agents is fundamentally different from prompting for one-off completions. You're designing instructions for a system that will reason, plan, and act over multiple steps—often without human intervention.

System Prompt Structure

A well-designed agent system prompt typically includes these components:

# Agent Identity and Role
You are a [specific role] agent that helps users with [domain].
Your goal is to [primary objective].

# Capabilities and Constraints
You have access to the following tools:
- tool_name: Description of what it does and when to use it
- tool_name_2: Description with specific conditions

You CANNOT:
- [Explicit boundary 1]
- [Explicit boundary 2]

# Operating Procedures
1. Before taking any action, explain your reasoning
2. Break complex tasks into numbered steps
3. After each tool call, verify the result before proceeding
4. If uncertain, ask for clarification rather than guessing

# Response Format
Use this format for tool calls:
<tool_call>
{{"tool": "tool_name", "parameters": {{"param": "value"}}}}
</tool_call>

# Error Handling
If a tool returns an error:
1. Analyze the error message
2. Determine if retry is appropriate
3. If stuck after 2 attempts, explain the issue to the user

# Current Context
Working directory: {cwd}
Current time: {timestamp}
User preferences: {preferences}

Key Prompting Techniques

Sources: Augment Code: 11 Prompting Techniques, PromptHub: Agent Prompting, OpenAI Guide

Memory and State Management

Memory is what separates sophisticated agents from simple chatbots. According to recent research, as foundation models scale toward trillions of parameters and context windows reach millions of tokens, the computational cost of remembering history is rising faster than the ability to process it.

Implementation Patterns

Testing Strategies

Testing agentic systems is fundamentally different from testing traditional software. Agents are non-deterministic, their behavior depends on LLM outputs that can vary, and even minor changes to prompts or models can cause unpredictable results.

Critical Testing Approaches

Sources: Applause: Agentic AI Testing, TestGrid: Autonomous QA

Deployment Patterns

Moving from prototype to production requires careful consideration of deployment architecture. According to The New Stack, the agentic AI field is going through its "microservices revolution"—single all-purpose agents are being replaced by orchestrated teams of specialized agents.

Multi-Agent Orchestration

Deployment Strategies

Cost Optimization

According to Machine Learning Mastery, the 2026 trend is treating agent cost optimization as a first-class architectural concern—similar to how cloud cost optimization became essential in the microservices era.

Cost Optimization Strategies

Governance and Safety

Unlike traditional software that executes predefined logic, agents make runtime decisions, access sensitive data, and take actions with real business consequences. According to Deloitte's Tech Trends 2026, governance is critical as agents gain autonomy.

Essential Governance Framework

For a deeper dive into risk management, see our article on Agentic AI Risks, Governance, and Safety.