Operations | Monitoring | ITSM | DevOps | Cloud

This comprehensive guide walks you through installing Terraform with a focus on security hardening and scalable infrastructure automation. You'll learn installation steps across platforms, configuration best practices, and how to set up Terraform for production-ready IaC deployments that grow with your organization's needs.

Discover 8 critical questions engineering leaders must ask to measure and improve developer productivity. Learn how to assess team velocity, identify bottlenecks, and leverage DORA metrics for data-driven decisions that enhance engineering performance and developer experience. Why does developer productivity feel like it's declining even as your team grows? You hire more engineers, yet features ship slower. Sprint velocity looks healthy on paper, but deployment frequency tells a different story.

Engineers often ignore cloud costs due to lack of visibility, misaligned incentives, and disconnected workflows. This guide explores the root causes and provides actionable strategies to embed cost awareness into engineering culture, including automation, real-time feedback, and FinOps best practices that make cost optimization a natural part of the development process.

Engineering teams often deploy code much faster than they can safely release new features to users. This gap can create risks if releases skip testing, approvals, or gradual rollouts. Feature flags help by separating deployment from release, so you can ship code continuously and control which features users see through configuration.

Incorporating robust security measures into feature flag management is critical to protecting sensitive data and maintaining compliance. Harness FME security features, like remote evaluations in Thin SDKs and governed AI flag cleanup, let you practice security by design and standardize solid security practices across your teams.

A DevOps toolchain that scales is the smallest unified stack with central governance and golden paths, not the longest list. 71% of teams say context-switching drains productivity; 73% of leaders report barely any teams have golden paths (Harness). AI coding speed stresses the after-code stages where DevOps toolchain sprawl creates the biggest governance gaps. Unified platforms keep governance, verification, and rollback consistent as AI raises code volume entering the pipeline.

‍ Here is a story platform engineering teams know by heart: developers find a shiny new tool, start building at a breakneck pace, and before you know it, the organization is drowning in a massive wave of unmanaged components. Right now, that exact story is playing out with generative AI.

‍Teams building agents have converged on something that looks a lot like the software development lifecycle, but reshaped around a system whose output isn't deterministic: prototype an agent against a framework, evaluate it against a dataset of expected behavior, deploy it somewhere real, observe how it behaves against live traffic, and feed what you learn back into the next prototype. Call it the agent development lifecycle (Agent DLC).

AI agents fail differently from the software we spent the last two decades learning to monitor. We hear some version of the same story from teams shipping agents to production: an agent starts producing wrong answers. Not obviously broken: confident, well-formatted, plausible wrong. The logs are clean, latency looks healthy, and error rates sit at zero. Nothing flags a problem. A user eventually does.

Software release management moves code through testing, approval, and into production with a clear rollback plan. 72% of organizations have hit a production incident from AI-generated code; developers now ship 63% faster (Harness, 2025). Effective release management needs defined stages, approval gates, automated testing, and the ability to roll back quickly. Feature flags, automated safety gates, and progressive delivery let teams ship faster and safer as AI raises code volume.