Agentic AI

Agentic AI represents a significant evolution in artificial intelligence systems. Unlike traditional AI models that simply respond to prompts, agentic AI systems can proactively take actions to accomplish complex goals. This post introduces the fundamental concepts of agentic AI and the critical distinction between workflows and agents.

Table of contents

What is Agentic AI?

Agentic AI refers to AI systems designed to act as agents - entities that can perceive their environment through inputs and act upon that environment through outputs to achieve specific goals. [1] These systems combine several capabilities:

1. Goal-directed behavior: Working toward achieving specific objectives
2. Autonomy: Making decisions with minimal human intervention
3. Tool usage: Leveraging various tools and APIs to accomplish tasks
4. Planning: Creating and adjusting strategies to reach goals
5. Learning: Improving performance based on feedback and experience

The Critical Distinction: Workflows vs. Agents

In the agentic AI space, there are two primary architectural approaches:

Agentic Workflows

Workflows are systems where LLMs and tools are orchestrated through predefined code paths. These systems:

• Follow explicit, developer-defined sequences of operations
• Have limited or no ability to deviate from prescribed paths
• Are highly predictable and reliable
• Require complete anticipation of all possible scenarios
• Are ideal for regulated industries where predictability is crucial

# Simplified example of an agentic workflow
def customer_support_workflow(customer_query):
    # Predefined path for handling queries
    query_category = classify_query(customer_query)
    
    if query_category == "billing":
        response = handle_billing_query(customer_query)
    elif query_category == "technical":
        response = handle_technical_query(customer_query)
    else:
        response = handle_general_query(customer_query)
    
    return response

AI Agents

Agents are systems where LLMs dynamically direct their own processes and tool usage. These systems:

• Determine their own action sequences based on goals
• Can adapt to unforeseen scenarios
• Have greater flexibility but potentially less predictability
• Make autonomous decisions about which tools to use and when
• Excel in complex, variable environments

# Simplified example of an AI agent
def customer_support_agent(customer_query):
    # Agent decides how to handle the query
    agent = Agent(
        goal="Resolve customer issue effectively",
        tools=[database_tool, knowledge_base_tool, escalation_tool]
    )
    
    # Agent chooses its own path and tools
    response = agent.solve(customer_query)
    return response

When to Use Each Approach

Choosing between workflows and agents depends on your specific needs:

Use Workflows When:

• Predictability is essential
• Processes are well-defined and rarely change
• Compliance and audit requirements are strict
• You need guaranteed behavior within known parameters

Use Agents When:

• Problems are varied and unpredictable
• Flexibility and adaptability are priorities
• The environment changes frequently
• Creative problem-solving is beneficial

Conclusion

Understanding the distinction between agentic workflows and AI agents is crucial for developing effective AI solutions. Both approaches have their place in the AI ecosystem, and the right choice depends on your specific needs, risk tolerance, and use case.

In future posts, we’ll dive deeper into implementation details, best practices, and case studies for both agentic workflows and AI agents. We’ll also explore hybrid approaches that combine the best of both worlds for optimal results.

Stay tuned to learn how to leverage these powerful approaches in your own projects!

References

1. Building Effective Agents