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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Traditional Software Engineering is Dead!

**[Pavan Belagatti](https://daily.dev/sources/dfa0u7-fs)** · [@johhnypav](https://daily.dev/johhnypav) · 2 min read · 18 upvotes · 4 comments

## Summary

Traditional software engineering roles are being reshaped by AI agent workflows, where specialized agents (Code, Test, Security, DevOps, SRE) collaborate to automate the full development lifecycle. These model-agnostic agents can be powered by any LLM and operate on real engineering infrastructure like GitHub, Jira, Kubernetes, and Datadog. A context layer grounds each agent with system instructions, memory, and tools, enabling them to take real actions such as opening PRs or deploying services. The system runs as a continuous feedback loop — plan, execute, validate, learn — distinguishing agentic automation from traditional scripting.

## Content

Today, smart developers are not the ones that work with tools & platforms, the smart ones are those who delegate their work to AI Agents & workflows. Nobody writes code now, nobody does code reviews & same goes with testing, CI/CD, etc. Now, these things can be assigned to a specialized Agents (with proper guardrails, context lake & human gates set) - This is Agent Engineering or AI-Driven software engineering for you.

AI-driven software engineering is where specialized agents like Code, Test, Security, DevOps, and SRE, etc collaborate to automate the development lifecycle. Rather than relying on a single monolithic AI, each agent owns a distinct domain, enabling focused expertise and parallel execution.

These agents are model-agnostic, meaning they can be powered by any LLM (GPT, Claude, Gemini, Llama), allowing teams to optimize for cost, speed, or capability per task. What makes the agents effective is the context layer, which injects each call with system instructions, available tools, retrieved knowledge, short- and long-term memory, formatting rules, and conversation history, turning generic model outputs into grounded, role-aware actions.

The agents operate on real engineering infrastructure - GitHub, GitLab, Jira, Slack, Kubernetes, Datadog, Terraform, etc - meaning they don't just reason but actually take actions like opening PRs, deploying services, or triaging incidents. The entire system runs as a continuous feedback loop: plan the task, execute across tools, validate with tests and scans, learn from the outcome, and repeat until the goal is met.

This iterative, self-correcting design is what distinguishes agentic automation from traditional one-shot scripting, enabling increasingly autonomous engineering workflows.

I recently created a hands-on guide to Agentic SDLC, here: [https://youtu.be/VWB-6_A5XH8](https://youtu.be/VWB-6_A5XH8)

## Community discussion

Top comments from developers on daily.dev.

**@tgjzd** · 6 upvotes

> "Nobody writes code now, nobody does code reviews & same goes with testing, CI/CD, etc."
>
> It's simply not true. So the whole text is based on false premise.

**@daniel8000** · 2 upvotes

> All this sophisticated automation to create an app nobody needs.

**@celom** · 0 upvotes

> I agree that we should deviate from _the code_ as a backbone for the product. But I'm still to find a pattern that proves this is viable in brownfield projects - everything is great in greenfield... On that note: [https://dly.to/FSs8R1MNaCJ](https://dly.to/FSs8R1MNaCJ) :)

---

Tags: [#devops](https://daily.dev/tags/devops), [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents)

[View this post on daily.dev](https://daily.dev/posts/traditional-software-engineering-is-dead--kxtgh3pbg)

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