The Latest and Popular SDLC Topics

From Pilot to Production: The Six Agent Patterns That Determine Whether Your AI Program Scales or Stalls

Most AI agent programs don't fail because of technology. They fail because nobody owns the agent and nobody monitors it.

July 2, 2026

by BALAJI BARMAVAT

· 1,581 Views · 1 Like

Multi-Agent Software Engineering: One Coding Agent Isn't Enough

Multiple AI agents with clear roles and checks deliver real software better than one agent, but cost more and only suit large tasks.

July 2, 2026

by Jithu Paulose

· 1,966 Views

Dead Letter Queue Patterns in Apache Flink: Handling Poison Messages Without Stopping Your Stream

A poison message can trap a Flink job in a restart loop. Use side outputs, retries, tiered DLQs, durable sinks, and replay jobs to keep the stream running.

July 2, 2026

by Rohit Muthyala

· 2,487 Views

Why AI-Generated Code Is Making Regression Testing More Important, Not Less

AI-generated code introduces integration failures that spec-based tests cannot catch. Regression testing grounded in real production behavior is the fix.

July 1, 2026

by Sancharini Panda

· 1,597 Views

AI-Augmented React Development: How I Rebuilt My Workflow Without Losing Control of the Code

AI accelerates React 18 workflows but breaks down in large enterprise codebases. Here’s where it helps, where it fails, and the guardrails your team needs.

July 1, 2026

by Sathwik Nagulapati

· 2,118 Views · 1 Like

If You Can Facilitate a Retrospective, You Can Audit Your AI

Learn how the AI Delegation Audit helps Scrum teams inspect AI workflows, catch automation drift, and keep delegated AI work safe and accountable.

July 1, 2026

by Stefan Wolpers

CORE

· 1,697 Views · 1 Like

Loop Engineering: The Layer After Prompt, Context, and Harness Engineering

This article walks through all four layers side by side, with comparison tables for when to use each one and which agent architecture fits which job.

July 1, 2026

by Vidyasagar (Sarath Chandra) Machupalli FBCS

CORE

· 1,835 Views · 1 Like

The New Senior Developer Job Description: Half Engineer, Half AI Systems Architect

Senior developers now own two roles: traditional engineering plus AI systems architecture. This split reshapes compensation, hiring, and what 'senior' actually means.

June 30, 2026

by Dinesh Elumalai

CORE

· 3,079 Views · 4 Likes

Architecting Trustworthy AI: Engineering Patterns for High-Stakes Environments

This post presents three domain-agnostic engineering patterns for building AI systems that remain safe even when the model is wrong.

June 29, 2026

by Sujay Puvvadi

· 1,511 Views

Building Production-Safe Agentic Remediation With Docker MCP Gateway: Lessons From 43% to 100% Accuracy

We built an AI Docker remediation system on MCP Gateway. First version: 43% correct. After 9 engineering fixes: 100%. Here's what changed.

June 29, 2026

by Mohammad-Ali Arabi

· 2,331 Views

Black Swan Bugs: Paving the Way for New Roles in Software Engineering

By outsourcing more of our thinking to probabilistic systems, we risk weakening the very human habit black swans demand: the habit of making the right questions.

June 29, 2026

by Stelios Manioudakis

CORE

· 3,155 Views · 3 Likes

Why Requirements Are Becoming the Control Layer in AI-Assisted Development

As AI generates more code and tests, requirements become the control layer that keeps delivery consistent, traceable, and aligned with the system context.

June 29, 2026

by Andrei Lavygin

· 1,406 Views · 1 Like

Before the AI Coding Agent Writes Code: Structuring Scattered Requirements With PARA

AI coding agents often fail when the required context is scattered. It is about preparing better context before the agent writes code.

June 29, 2026

by Venkata Naga Satya Sai Vineeth Kondisetty

· 1,132 Views

The New Insider Threat Isn't Human: Securing AI Agents Before They Secure Themselves

AI agents are becoming powerful insiders. Learn how identity, MCP security, least privilege, and policy enforcement reduce emerging risks.

June 26, 2026

by Igboanugo David Ugochukwu

CORE

· 2,110 Views · 1 Like

Data Pipeline Observability: Why Your AI Model Fails in Production

Your machine learning model had 95% accuracy in testing, but crashes in production. The problem isn't the model, it's your data pipeline.

June 26, 2026

by Abhilash Rao Mesala

· 1,670 Views · 1 Like

Two Clocks Are Running Out at Once, and Almost Nobody Is Watching Both

Quantum computing and AI coding tools are changing security. Learn why crypto-agility and better governance are now critical.

June 26, 2026

by Igboanugo David Ugochukwu

CORE

· 2,176 Views · 2 Likes

What Cloud Engineers Actually Need to Know About AI Infrastructure

AI infrastructure isn’t about GPUs. Most issues come from storage, networking, data pipelines. If GPU utilization is low, check the infrastructure first, not the model.

June 26, 2026

by Naveen Kalapala

· 1,573 Views · 1 Like