Why organizations need to implement Agentic AI?
Highlights
Using AI chats / Coding assistants will not work to complete complex projects inside a larger organization. Hear me out, coding assistants are great to boost individual productivity, but when it comes down to a bigger project you need to build a team. And that’s where it comes down to Agentic AI workflow implementation vs having individuals using AI for their day to day tasks.
A great example of Agentic AI workflow within software development lifecycle where human engineers are orchestrating and controlling the process or AI-coding-agents.
Agentic-AI in practice: A Coding Workflow Example
Imagine developing a complex software feature — such as a real-time analytics dashboard. In traditional setups, multiple engineers manually handle diverse tasks: front-end design, backend API integrations, data processing logic, and performance optimizations.
Here's how an Agentic-AI coding workflow blends into a traditional SDLC process:
- Kick-off: Software Engineer clearly defines goal, objectives and high-level requirements and passes the information to AI Architect
- Task Decomposition: AI Architect agent autonomously analyze the goal, breaking it down into detailed, manageable subtasks.
- Task Review: Software Engineer performs “Prompt review” process, to make sure that tasks and instructions are clear enough to be passed on AI-agents
- AI-Agent Allocation: Each subtask is automatically allocated to specialized AI-agents — expert systems trained in front-end technologies, backend frameworks, database optimization, or code security.
- Execution: AI agents concurrently execute subtasks, each optimizing its solution iteratively through continuous learning loops.
- Integration and Review: AI agents integrate their outputs into a unified solution, with the human developer conducting final code reviews, refinement, and strategic oversight.
Now, the most important revelation is that it is not just about automation, you don’t have to build whole automated workflow to have your AI-agents team build everything for you.
You can start small by playing around and breaking this process into smaller pieces, replicating it manually and learning what works best for your project. Refine, iterate and start shifting SDLC process from individual AI coding assistants to a holistic AI-team where your engineers can collaborate with AI-agents.