Agentic AI 101: A Quick Introduction

As the dust begins to settle around Generative AI and its productivity benefits, we are witnessing the rise of Agentic AI, which promise to be far more powerful. Major tech companies are making significant investments in AI Agents, and Gartner has projected that Agentic AI will be the top technology trend in 2025. Here's what industry leaders are saying about Agentic AI:

• Mark Zuckerberg believes there could eventually be more AI agents than people in the world.

• Nvidia CEO Jensen Huang envisions AI agents playing critical roles across various sectors, including marketing and chip design.

What is Agentic AI?

Agentic AI is driven AI Agents operating on top of large language models (LLMs) and is an autonomous system that understands, thinks, and acts by processing given instructions. It transforms AI intelligence into actionable steps, following a sequence of instructions to perform tasks, and continuously learns through a cycle of instructions, processing, action, and improvement. In simple words its the next wave of AI built on top of Gen AI.

For example, an AI email assistant processes incoming emails to understand their context, drafts replies based on the content, and can even send responses autonomously.

Why Agentic AI is Needed?

Generative AI, in its current form, is bi-directional: users provide prompts, and LLMs generate responses. However, it cannot autonomously perform tasks, make decisions, or operate without continuous human intervention.

AI agents, on the other hand, work on top of LLMs and are designed to automate tasks, make decisions, and solve problems without constant supervision. They save time, reduce errors, and boost efficiency by handling repetitive or complex tasks, offering personalized assistance, and enabling smarter decision-making. When tens, hundreds, or even thousands of AI agents collaborate, they unlock massive scalability and productivity, transforming workflows across industries.

What is AI Agent's Potential in Software Engineering?

AI Agents have immense potential in software engineering, particularly within the Software Development Life Cycle (SDLC). Advanced AI Agents can work in sequence, where the output of one becomes the input for the next. This creates a multiplier effect on scalability, productivity, and efficiency.

For instance, starting with project requirements, AI agents can collaborate seamlessly to produce the final output, significantly streamlining the entire SDLC process. Feedback loops enable these agents to learn and improve over time, facilitating continuous improvement.

Example sequential workflow of AI Agents in SDLC: AI Agents can integrate advanced workflows to take business requirements as inputs and, after passing through various stages, deploy code to production while generating user documentation.

1. Requirement Analysis AI Agent

   • Extracts and refines project requirements from inputs like client documents, emails, or interviews.

   • Output: Structured and detailed requirements document or data.

2. UI/UX Design AI Agent

   • Creates prototypes, wireframes, and visual designs based on the requirements.

   • Output: UI/UX designs and design specifications.

3. Code Generation AI Agent

   • Converts UI/UX designs and requirements into working application code.

   • Output: Codebase with functionality matching the design and requirements.

4. Automated Testing AI Agent

   • Analyses the code, generates test cases, executes tests, and evaluates results.

   • Output: Test results, reports, and identified issues.

5. Bug Detection and Fixing AI Agent

   • Reviews test results, identifies bugs, and generates patches or fixes for the issues.

   • Output: Debugged and functional code.

6. DevOps Automation AI Agent

   • Automates CI/CD pipelines for building, testing, and deploying the application to staging or production.

   • Output: Deployed application or service.

7. Code Refactoring AI Agent

   • Improves code structure, readability, and efficiency without changing functionality.

   • Output: Clean, optimized code.

8. Documentation AI Agent

   • Creates technical documentation, API guides, or user manuals based on the final codebase and workflows.

   • Output: Complete project documentation.

9. Knowledge Sharing AI Agent

   • Summarizes the project, creates onboarding guides, and generates training materials for new team members.

   • Output: Knowledge base, training documents, or video tutorials.

Conclusion

Agentic AI is a big step forward in how we use artificial intelligence. Unlike regular AI, they can work on their own, learn, and handle complex tasks without constant help. By saving time and improving efficiency, AI Agents are set to transform industries like software development, making work faster and smarter.