The Missing Runtime for Long-Running AI Agents
Enterprise AI agents need more than stronger models. They need durable execution environments that can coordinate multi-step workflows, survive failures, pause for human review and resume reliably after disconnects or delays. AI Agents Have Moved Beyond Chat Demos AI agents work beautifully in demos. A user asks a question, a large language model generates a […]
Automated Diagnosis Isn’t Automated Understanding: What Postmortems Teach Us About Building Trustworthy Incident AI
AI incident tools can reduce alert noise, but real root-cause diagnosis requires causal reasoning, live dependency context, uncertainty handling and strong postmortem data.
AI Can Generate Your Infrastructure. Can Your CI/CD Pipeline Trust It?
AI-generated infrastructure code is exposing a growing security gap, pushing platform teams to add stronger automated gates, provenance tracking and human review before Terraform, Kubernetes and CI/CD changes reach production.
CI/CD for AI-Enabled Applications: Why Traditional Deployment Pipelines Need to Evolve
Traditional CI/CD pipelines are optimized around a familiar assumption: source code changes, automated tests validate the change, a build artifact is produced, and the application is promoted through environments. AI-enabled applications complicate that model because production behavior can change even when application code does not. A new model version, feature transformation, prompt configuration, retrieval index […]
Why “Tokenmaxxing” Was Always the Wrong Way for Developers to Measure AI Productivity
The term “tokenmaxxing” left the developer lexicon just as quickly as it arrived, and like most viral technology concepts, it means different things depending on who’s using it. In practice, the term is being applied in two ways: maximizing total token consumption as a proxy for AI adoption and effort, or optimizing output per token […]
Beyond Log Search: What We Learned Building a RAG-Based Incident Diagnosis System
A RAG-based AIOps framework can cut incident diagnosis time by grounding LLM reasoning in real runbooks, tickets and postmortems, improving root-cause accuracy while giving SREs source-backed answers they can trust
Scalable Jenkins Management: Empowering Enterprises With Centralized Control for 150+ Instances
A centralized Jenkins control plane can reduce fleet sprawl, automate upgrades and backups, improve observability, and give application teams safe self-service across large multi-cloud CI/CD estates.
Preparing Infrastructure for the Next Phase of Agentic AI
Agentic AI is changing government infrastructure requirements, pushing agencies to rethink workflows, observability, data movement and resource prioritization before investing in new hardware.
Production-Grade AI Eval Systems. What I Learned Putting LLMs on Call
Production-grade AI reliability requires more than uptime and latency. A layered eval system helps teams detect hallucinations, RAG failures and quality regressions before customers do.
Waterfall 2.0: Controlling LLM-Driven Software Development with Stage-Gated Discipline
Waterfall 2.0 reimagines classic stage-gated software development for the LLM era, combining fast AI generation with structured validation, cheap backtracking and a shared knowledge corpus.
Why API Test Generation Is a Judgment Problem, Not a Code Generation Problem
When we started using large language models for API test generation at KushoAI, the results were impressive on the surface. Tests appeared in seconds. Coverage breadth went up. The team was excited. Then we looked more carefully at what was being produced. On one endpoint, the model generated 26 tests where a senior QA engineer […]
Why Cryptographic Inventory Is the First Step Toward Quantum Readiness
Post-quantum readiness starts with visibility. DevOps teams need a continuous cryptographic inventory to map algorithms, keys, certificates, libraries, infrastructure and third-party dependencies before PQC migration begins.
Elevating Technical Documentation: Using Predictive AI to Standardize Visual Knowledge Bases
Every release changes documentation alongside the code. Developers depend on that documentation during implementation, maintenance and troubleshooting. A diagram becomes misleading once services are renamed or integrations change without a corresponding documentation update. Keeping documentation updated becomes increasingly difficult as repositories, products and release cycles grow. Outdated or inconsistent images eventually find their way into […]
How to Avoid Repeating the “Automate Everything” Mistake Due to AI FOMO
The DevOps community has already experienced technological extremes. You might recall the time when automation was thought of as a panacea for all possible engineering issues, and the slogan “Automate Everything” was ringing out everywhere. Over time, the industry came to an important conclusion: Automation should not become the end goal. Along with faster software […]
Why Self-Healing Tests Need a Deployment Gate
When an end-to-end test fails after a front-end change, the repair often looks routine. A class name changed. A button moved. A selector that used to be unique now matches two elements. A self-healing system inspects the page, proposes a new locator, reruns the test, and gets a green result. That green result is useful, […]
Are LLMs Equally Good (or Bad) at Building Secure Software?
An SCW study finds a large variance in how frontier and budget LLMs perform across different frameworks, and explains the striking cost variance.
What the Microservices Era Can Teach Us About AI
AI agents are not just microservices with LLMs attached. Their long-running, non-deterministic workflows demand durable execution, per-step identity, governance and observability.
Is Your New DevSecOps Tooling Reducing Work Or Just Adding to It?
Security belongs in the software delivery pipeline. The harder question is where, how often and at what cost. Many pipeline teams eventually add security scanning to CI/CD, and relatively few go back afterward to measure what it actually cost the delivery process. Security coverage went up. Something else changed too, and it is rarely measured […]
Treat Business Workflow Changes Like Deployments
Business automation often reaches production without the release discipline applied to application code. A routing rule changes, an approval threshold moves, or an integration starts writing to a new system. The edit may happen in a visual builder instead of a repository, but its blast radius is still real: orders can duplicate, customers can receive […]
Developer Resistance to AI Isn’t Fear – It is Identity
Developer resistance to AI is less about job loss than a deeper shift from hands-on coding to supervising, validating and orchestrating AI-generated work.
Production-Safe Testing: The Missing Piece in Most DevSecOps Strategies
Most DevSecOps teams invest heavily in security before deployment, yet attackers target the production environment where applications, APIs, and user behavior are constantly changing. If security validation stops before release, critical risks can remain hidden until they are exploited. The gap is more common than many organizations realize. A survey found that over 70% of […]
Reducing MTTR: A Practical Guide to Correlating Incidents with AIOps
AI-driven incident correlation helps SRE and DevOps teams reduce alert noise, identify root causes faster and improve MTTR by connecting related metrics, logs and traces.
Developer’s Checklist: How to Build an FHE Application
A practical guide to building fully homomorphic encryption applications, covering architecture, noise budgets, polynomial approximations, parameter choices, SIMD packing, testing and performance optimization.