Exploring Agentic AI

Introduction

Businesses are constantly seeking innovative solutions to streamline operations, enhance productivity, and gain a competitive edge. Traditional software applications, while powerful, often struggle to adapt to complex, dynamic business requirements. Enter Agentic AI – a groundbreaking technology that promises to revolutionize how businesses approach problem-solving, decision-making, and operational efficiency.

What is Agentic AI?

An AI agent is an AI system designed to solve problems end-to-end. Unlike traditional software or basic AI tools, these agents can:

• Make independent decisions
• Understand complex business contexts
• Work across multiple systems and interfaces
• Continuously learn and improve their performance

Where generative AI creates content, AI agents develop comprehensive solutions, completing not just what needs to be done, but in the most effective way.

How Does Agentic AI Work?

Agentic AI operates through a strategic four-step process:

1. Perceive

The system collects and analyzes data from diverse sources, extracting meaningful insights. This goes beyond simple data gathering, focusing on understanding the context and identifying critical information.

2. Reason

A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, vision processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.

3. Act

By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.

4. Learn

Each interaction feeds into a feedback loop. The AI agent becomes progressively more effective, refining its decision-making processes based on accumulated experience and feedback.

Agentic AI in Software Development

In software engineering, AI agents are revolutionizing traditional approaches:

• Accelerating code generation
• Proactively identifying and resolving bugs
• Optimizing project management and resource allocation
• Predicting potential technical challenges before they emerge

Agentic AI promises to transform AI coding assistants, or copilots, into smarter software development tools that write large pieces of code. While coding assistants have received mixed reviews so far, analyst firm Gartner predicts that smarter AI agents will write the majority of code within three years, leading to a need for most software engineers to reskill.

Coding agents will not only write the code, but separate agents will review code for errors, says Sheldon Monteiro, executive vice president and chief product officer at Publicis Sapient, a digital transformation advisory firm.

We have long known that organizations have been relying on simple chatbots and voice bots to handle simple customer service requests, but agentic AI will allow customer service automation to evolve into a more robust service that doesn’t just answer a few frequently asked questions.

Conclusion

Agentic AI represents more than a technological advancement – it's a fundamental shift in how businesses interact with digital systems. By combining autonomous reasoning, adaptive learning, and strategic execution, AI agents offer unprecedented potential for operational efficiency and innovation.

Ready to explore how Agentic AI can transform your business? Contact us for a consultation.