Operations | Monitoring | ITSM | DevOps | Cloud

Database changes are inherently complex: coordinating schema updates, managing risk, and avoiding downtime all require care. Even when teams improve how they deliver those changes, governance often remains inconsistent, manual, and reactive. In many environments, governance is treated as a separate layer around deployment. Policies are applied unevenly, approvals become bottlenecks, and audit evidence is assembled after the fact, creating gaps in enforcement and increasing operational risk.

Regular DR testing is no longer a compliance checkbox — it is a critical engineering discipline that determines whether an organisation can survive a real cloud outage with its services and revenue intact. As the AWS Middle East incident demonstrated, regional cloud failures can strike without warning and defeat standard redundancy models, making untested DR plans dangerously unreliable.

Security teams face overwhelming amounts of data and complex interfaces, making it hard to access critical insights. AI tools promise solutions, but integration remains difficult as time ticks away and leadership wants the latest data to inform risk decisions. Most security platforms lack seamless integration, slowing access to important data and hindering AI-powered workflows.

Chatbots are becoming ubiquitous. Customer support, internal knowledge bases, developer tools, healthcare portals - if it has a user interface, someone is shipping a conversational AI layer on top of it. And the pace is only accelerating. But here's the problem nobody wants to talk about: we still don’t have a reliable way to test these chatbots at scale. Not because testing is new to us. We've been testing software for decades.

AI is quickly becoming part of the engineering workflow. Teams are experimenting with assistants and agents that can answer questions, investigate incidents, suggest changes, and automate parts of software delivery. But there is a problem hiding underneath all of that momentum. Most engineering environments were not built to give AI the context it needs. In many organizations, the service catalog lives in one place. Deployment data lives in another. Incident history sits in a separate system.

Self-service infrastructure replaces ticket queues with controlled, automated workflows so developers can get what they need safely and on demand. Policy-as-code, standardized templates, and an Internal Developer Portal (IDP) provide guardrails that maintain security, compliance, and cost control. You can demonstrate ROI in 90 days by starting with a single golden path and measuring adoption, speed, and policy outcomes. If platform teams are buried in tickets, they are not operating a control plane.

Your developers are buried under tickets for environments, pipelines, and infra tweaks, while a small platform team tries to keep up. That is not developer self-service. That is managed frustration. If 200 developers depend on five platform engineers for every change, you do not have a platform; you have a bottleneck. Velocity drops, burnout rises, and shadow tooling appears. Developer self-service fixes this, but only when it is treated as a product, not a portal skin.

When AI agents operate across a multi-module platform like Harness (from CI/CD to DevSecOps to FinOps), the number one goal is to give you answers that are correct, consistent, and grounded in real data. Getting there requires a deliberate architectural choice: when a question can be answered from structured platform data, the agent should use a schema-driven Knowledge Graph rather than raw API calls via MCP. The principle is simple: if the data is modeled, retrieval should be deterministic.

At SREday NYC 2026, the ShipTalk podcast spoke with Phil Christianson, Chief Product Officer at Xurrent, for a leadership perspective on the intersection of product strategy, engineering investment, and platform reliability. While many of the conversations at the conference focused on tools, automation, and incident response, Phil offered a view from the C-suite level, where decisions about engineering priorities and R&D investment ultimately shape how reliability practices evolve.

‍Why 90% of AI prototypes never make it to production, and what to do about it. Every week, someone on my team shows me a demo that looks incredible. An agent that writes deployment pipelines. A chatbot that triages incidents. A copilot that generates test cases from Jira tickets. The demo takes 20 minutes. The audience claps. Everyone leaves convinced we're six weeks from shipping it. We're not.