Operations | Monitoring | ITSM | DevOps | Cloud

CTO Predictions for 2026: How AI Will Change Software Development | ShipTalk S4E7 Special Episode

In this special ShipTalk episode, host Dewan Ahmed (Principal Developer Advocate, Harness) sits down with @Harnessio Field CTO Nick Durkin for spicy—but practical—2026 predictions across AI, software delivery, DevSecOps, MLOps, and developer experience. Will we see the first “AI-caused meltdown”? Are AI “confidence scores” even trustworthy? Is 2026 the year of AI cleanup crews and recovery engineering? Nick’s take: the answer isn’t more gates—it’s guardrails, policy in the pipeline, and teams operating with the same “rulebook.”

CTO Predictions for 2026: Special ShipTalk Episode with Nick Durkin

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.

Theory to Turbulence: Building a Developer-Friendly E2E Testing Framework for Chaos Platform

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.

ShipTalk S4E6 | Beyond the Magic Box: Solving AI Hallucinations with Precision RAG

In this episode of the ShipTalk Podcast, host Dewan Ahmed (Principal Developer Advocate at Harness) sits down with Evgeny Ilinykh (Founder of GuidedMind.ai and former Tesla Engineering Manager) to move past the AI hype and get into the engineering reality of Retrieval-Augmented Generation (RAG). If your AI agents are hallucinating, the problem probably isn't your model—it’s your retrieval layer. Evgeny breaks down how to turn the "black box" of LLMs into a transparent, production-ready system that developers can actually trust.

Knowledge Graph + RAG: A Unified Approach to DevOps Intelligence

Knowledge graphs and RAG (Retrieval-Augmented Generation) are complementary techniques for enhancing large language models with external knowledge, and each brings unique strengths for DevOps use cases. While they are often mentioned together, they are fundamentally different systems, and combining them delivers far better outcomes than relying on either approach alone.

How Enterprises Modernize and Migrate to the Cloud Safely with Harness Automation

Cloud migration is a multi-layer transformation involving infrastructure, CI/CD, governance, security, and cost management—not just application movement. Enterprises face unique migration challenges due to complex systems, parallel cloud operations, compliance requirements, and tool sprawl. Automation and standardization are critical to reducing risk, manual effort, and operational inconsistency during cloud-to-cloud migrations.

Harness Database DevOps Now Supports Google AlloyDB

Harness Database DevOps now natively supports Google AlloyDB, enabling enterprises to manage PostgreSQL-compatible schema changes with CI/CD, GitOps, and policy-driven governance. Teams gain faster, safer, and fully auditable database delivery while reducing operational risk and manual overhead across environments. As organizations double down on cloud modernization, Google Cloud’s AlloyDB for PostgreSQL is quickly becoming the preferred engine for mission-critical applications.

Capture and Use Network Response Data in AI Powered Testing

Learn how to capture and use response data from network calls to build smarter and more reliable AI-driven tests. This walkthrough covers the full workflow from configuring user actions to extracting backend responses, validating data, and creating dynamic test flows. You will also see how response data improves debugging visibility and supports data-driven automation. The video includes Ideal for developers, testers, and platform engineers looking to improve the accuracy and resilience of AI-powered test suites.

Accelerating Our Mission to Bring AI to Everything After Code

Since launching Harness in 2017, we’ve been on a mission to unlock faster innovation by removing the bottlenecks that slow software engineering teams down. From day one, we believed that the biggest obstacles in engineering weren’t in writing code — they were in everything that followed.

How Self-Service Workflows Transform Developer Productivity

Forget the ticket queues and slow handoffs. Harness Workflows let developers spin up services, environments, and everyday ops tasks in minutes. It’s self-service that’s fast, safe, and actually fun to use. A developer once told me, half-joking and half-frustrated, “I spend more time waiting than coding.” It wasn’t the dramatic kind of waiting, like an hour-long debugging session or a blocked deployment at midnight.