AgenticAI

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Agentic AI is a fundamentally different paradigm. Here, systems are built to perceive, reason, and act toward goals—often without constant human prompting.

An Agentic system includes:

• 𝗠𝗲𝗺𝗼𝗿𝘆: to retain and recall information over time.

• 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: to decide what actions to take and in what order.

• 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: to interact with APIs, databases, code, or software systems.

• 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: to loop through perception, decision, and action—iteratively improving performance.

Instead of a single model generating content, we now orchestrate 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, each responsible for specific tasks, coordinated by a central controller or planner.

This is the architecture behind emerging use cases like autonomous coding assistants, intelligent workflow bots, and AI co-pilots that can operate entire systems.

𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 We’re no longer designing prompts. We’re designing 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of interacting with the real world.

Step-by-Step: Build an Agentic Marketing Campaign System with OpenAgents

Step 1: Install Prerequisites

bash
pip install openai openagents langchain chromadb
        

Step 2: Define Your Agents

You’ll need at least 3 agents:

1. Strategy Agent

• Role: Breaks down marketing goal into subtasks.

from openagents import Agent

strategy_agent = Agent(
    name="StrategyAgent",
    system_message="You are a marketing strategist. Break campaign goals into a clear plan.",
)
        

2. Content Creator Agent

• Role: Writes posts, creates email templates, designs ads.

content_agent = Agent(
    name="ContentAgent",
    system_message="You are a content creator. Generate content for social media and emails using brand tone.",
)        

3. Execution Agent

• Role: Uses tools like schedulers, APIs (Twitter, Meta), and databases.

execution_agent = Agent(
    name="ExecutionAgent",
    system_message="You post content, monitor engagement, and adjust schedule using tools."
)        

Step 3: Connect Tools

You can add tools like:

Memory (ChromaDB or Weaviate)

Web access

Social media APIs (e.g., Meta Graph, Twitter/X, Buffer)

execution_agent = Agent(
    name="ExecutionAgent",
    system_message="You post content, monitor engagement, and adjust schedule using tools."
)        

Step 4: Use Shared Memory

from openagents.memory import SharedMemory

memory = SharedMemory()
strategy_agent.memory = memory
content_agent.memory = memory
execution_agent.memory = memory        

Step 5: Launch Multi-Agent Collaboration

from openagents import MultiAgentExecutor

executor = MultiAgentExecutor(
    agents=[strategy_agent, content_agent, execution_agent],
    memory=memory
)

executor.run("Launch a 4-week Gen Z campaign on Instagram for our eco-friendly water bottle.")        

How It Works in Action

User Input (Goal):

Strategy Agent:

Content Agent:

Execution Agent

Memory Module:

Legacy vs AgenticAI Campaign Management