Operations | Monitoring | ITSM | DevOps | Cloud

Harness has been recognized by TechStrong Group for its comprehensive, AI-native platform vision, winning Best End-to-End DevOps Platform, Best Platform Engineering Solution, and DevOps Industry Leader of the Year. At Harness, our mission has always been simple but ambitious: to enable every software engineering team in the world to deliver code reliably, efficiently, and quickly to their users, just like the world’s leading tech companies.

Integrating feature flag context into OpenTelemetry traces enhances observability by recording flag states as span attributes, making it easier to analyze how specific flags influence application behavior. When you toggle a feature flag, you're changing the behavior of your application; sometimes, in subtle ways that are hard to detect through logs or metrics alone. By adding feature flag attributes directly to spans, you can make these changes observable at the trace level.

This guide covers battle-tested chaos experiments for Kubernetes, AWS, Azure, and GCP to help you validate production resilience before real failures happen. Start with low blast radius experiments (pod-level) and gradually progress to higher impact scenarios (node/zone failures), always defining clear hypotheses and using probes to measure results. Building reliable distributed systems isn't just about writing good code. It's about understanding how your systems behave when things go wrong.

Have you ever asked yourself, what is the fastest way to turn a harmless Infrastructure as Code change into a production incident and an awkward postmortem? We did, and found that usually, it's from letting it through without any guardrails. Infrastructure guardrails in Infrastructure as Code (IaC) were once a nice-to-have. Today, they’re essential. Without clear boundaries and safety mechanisms, even well-designed IaC workflows can turn small mistakes into fast-moving, high-impact problems.

Our December Harness AI updates focus specifically on enhancements to Harness AI governance. You can review our Harness AI November updates here.

Harness FME continues its investment in OpenFeature, building on our early support and adoption of the CNCF standard since 2022. Evaluate flags consistently across languages and environments, and integrate them seamlessly into your applications without modifying your code. Feature flags are table stakes for modern software development. They allow teams to ship features safely, test new functionality, and iterate quickly, all without re-deploying their applications.

Harness Dynamic Pipelines offers an option to create pipelines, or pipeline stages, at runtime For a long time, CI/CD has been “configuration as code.” You define a pipeline, commit the YAML, sync it to your CI/CD platform, and run it. That pattern works really well for workflows that are mostly stable. But what happens when the workflow can’t be stable? In all of those cases, forcing teams to pre-save a pipeline definition, either in the UI or in a repo, turns into a bottleneck.

Over the past year, Harness’s India organization has entered a new phase of growth – one defined not just by scale, but by increasing technical depth and impact. What began as steady expansion has turned into real momentum across engineering, product, and operations. Today, 480 people work in India, contributing across every major product area. In 2025 alone, the team in India grew by more than 75%, and now each core Harness product offering has a strong engineering presence in the region.

AI will not fix broken software delivery. It will expose it. By 2026, teams that win will use specialist AI agents, guardrails over gates, and security built directly into the pipeline. As we look toward 2026, it is becoming clear that AI is not just changing how code is written. It is changing how software delivery itself works. The real shift is happening at the intersection of AI, security, and developer experience, where speed, risk, and responsibility now collide.

Chaos fault validation must be safe, predictable, and measurable. High setup friction blocks adoption and slows feedback loops. API-driven execution beats manual YAML workflows. Real-time logs and smart target discovery speed debugging. Dual-phase validation ensures impact and recovery. Strong DX enables faster, scalable chaos testing. As an enterprise chaos engineering platform vendor, validating chaos faults is not optional — it’s foundational.