Avya Chaudhary

How can knowledge mining turn everyday meetings into living architecture documentation? This article explains how LLMs extract architectural decisions, guidelines, and non-functional requirements from meeting transcripts. It walks through building an architecture harvesting pipeline, maintaining a reviewed knowledge base, and creating automated fitness functions that validate system designs, Jira tickets, and code against established architectural rules before changes reach production.

How do you build persistent AI agent teams without starting fresh sessions for every task? This hands-on guide walks through configuring an always-on Linux VM, organizing agents with Herdr, and enabling communication through Claude Code or Pi. It also shares practical examples, bootstrap scripts, and lessons on controlling scope, managing context, and avoiding unnecessary engineering work during execution.

Why are so many organizations underestimating enterprise AI costs? Most AI cost discussions focus on models and inference, but the real expense often sits in orchestration, observability, data pipelines, governance, and operational overhead. This guide examines every major AI cost layer and shows how enterprises can better forecast spending, measure ROI, and avoid budget surprises as AI moves into core operations.

What does it actually take for software delivery to be AI-native and not just AI-assisted? Gartner projects 80% of organizations will use AI-native platforms by 2030, but most are still running copilots on top of unchanged systems. This piece breaks down what mature AI-native delivery looks like, why it's genuinely hard to build, and what a real 8-stage operating model produces.

What if your AI security agent found the vulnerability but buried it under 46 other "critical" findings? That's not a win. We ran 15 AI coding agents against a real auth-boundary failure hidden inside 350,000 lines of code. The models that succeeded weren't always the biggest or most expensive, but those who understood priority and consequence.

Is 2026 really the year long-horizon agents go mainstream? We separate signal from noise, examine why the big demos succeeded in unusually favorable conditions, and lay out 5 concrete steps for making long-horizon agents work in production.

How do you understand what a legacy system actually does without reading 200,000 lines of code? This article explains a structured, AI-assisted pipeline that extracts business capabilities from code using signals like packages, schemas, and entry points. It shows how candidates are validated, refined, and mapped into a traceable domain model, enabling teams to move from raw code to actionable architecture in hours instead of weeks.

LLMs make many workflows technically possible, but not every task is ready for automation. This guide introduces a practical framework to evaluate automation viability using six signals: task volume, structure, time cost, error tolerance, complexity, and context dependency. The scoring matrix helps teams decide whether a workflow should be automated, assisted, or avoided before investing engineering effort.

Why do most organizations invest heavily in AI but struggle to see consistent results? The problem is not models or tools, but the absence of shared coordination. The answer lies in a hands-on AI Engineering CoE: a lean enablement engine that converts strategy into execution. See how to use a month-by-month roadmap to turn individual wins into reusable defaults and measurable engineering outcomes.

How can teams compare AI models when accuracy scores all look the same? LLM Chess offers a practical way to stress-test reasoning and reliability under repeated interaction. This guide explains how the benchmark exposes instruction failures, execution breakdowns, and prompt sensitivity, helping teams choose models that can sustain long-running, agentic workflows rather than just perform well on static tests.

Which AI platform truly delivers a context-aware SDLC agent for enterprise teams? In this deep comparison, we test CodeMie, Cursor, AWS, and n8n by building the same real-world user-story assistant. We evaluate setup complexity, accuracy, workflow orchestration, context indexing, tool support, and usability for non-developers. If you're evaluating AI platforms for SDLC digitization or enterprise automation, this breakdown shows which tool enables real adoption versus experimentation.

Explore how Spec-Driven Development streamlines brownfield code analysis through structured specs, reproducible workflows, and AI-assisted insights.

Can enterprises really turn AI from promise to performance? Many have the tools and data, but not the execution framework to move from pilots to production. See how to close the AI gap with a unified system for governance, adoption, and measurable progress that transforms experimentation into enterprise-scale intelligence.

What happens when AI generates code faster than teams can design, govern, or integrate it? Spec-driven development solves the cracks in code-first workflows by using living specifications as the execution source for humans and AI. This piece breaks down the stages, coordination benefits, and why brownfield systems, platform teams, and AI-heavy stacks are adopting this model.

Are you running copilots, testing agents, and piloting AI use cases, yet struggling to turn any of it into measurable outcomes? You’re not alone. Most enterprises are stuck between stages with no transition infrastructure. Learn how AI/Run converts fragmented AI efforts into orchestrated workflows with visibility, adoption, and ROI built in.

Why do so many enterprises know the AI adoption curve yet fail to scale it? Learn an enterprise-tested framework to close the execution gap, bridge bottlenecks, and turn pilots into organization-wide AI workflows that deliver real business impact.

Are your teams still experimenting with copilots or already orchestrating AI-native workflows? Explore the three stages of enterprise AI adoption with clear metrics, leader checklists, and playbooks to progress confidently. Learn how to move from

Discover how AI-native development is reshaping modern SDLCs. Learn how autonomous workflows, intelligent onboarding, and AI-powered delivery will shape the next era of software creation.