The Rise of Agentic AI

Autonomous agents are no longer a concept of the future; they are actively reshaping how we build and maintain software. In this post, we explore the transition from simple chatbots to complex agentic workflows.

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What is Agentic AI?

Unlike traditional AI models that respond to a single prompt, Agentic AI can:

• Decompose Tasks*: Break down a high-level goal into actionable steps.
• **Use Tools**: Access APIs, databases, and search engines to gather information.
• *Self-Correct: Monitor their own output and iterate until the goal is met.

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  Impact on Development

In the software development lifecycle (SDLC), agents are taking on roles such as: 1. Automated QA: Agents that can write and execute tests, then fix the bugs they find. 2. DevOps Orchestration: Managing deployments and scaling infrastructure based on natural language commands. 3. Drafting Documentation: Keeping technical docs in sync with code changes in real-time.

The future belongs to the "Human-in-the-loop" model, where developers act as architects of these agentic fleets rather than just writers of code.