Operations | Monitoring | ITSM | DevOps | Cloud

Moving On From MCP: How We Built the Bindplane AI Skill

If you've spent any time wiring AI coding agents into developer platforms over the last year, you've probably reached for MCP. We did too. And after enough sessions watching context windows balloon and tool calls misfire, we started looking for something different. This is the story of what we built instead — a native AI skill for the Bindplane CLI — and the engineering decisions behind it.

AI writes the code. Who delivers it safely? | Harness Blog

The question for enterprise AI in 2026 is no longer just which model. It’s which harness. An agent harness is the system around the model. It decides what the agent remembers, what context it sees, what tools it can call, what it is allowed to do, and what happens when it is wrong. The model provides intelligence. The harness provides control. This is where the real engineering is happening.

From PR to Production Without Leaving Your Cursor IDE | Harness Blog

TLDR: Today, Harness is introducing the Harness Cursor Plugin, bringing the power of the Harness AI-native software delivery platform directly into Cursor. This integration, along with the Harness Secure AI Coding hook for Cursor, allows developers and AI agents to move from code changes to vulnerability detection, CI/CD execution, security validation, approvals, deployments, and operational insight without leaving the editor. AI has completely changed how we write code.

7 best AI deployment platforms for production Kubernetes workloads in 2026

Training a model in a notebook is easy. What breaks teams is the step after, serving it reliably without haemorrhaging cloud budget or burying your SREs in YAML. The common trap: picking a platform that handles the model but not the surrounding stack. An AI deployment platform should orchestrate the full application graph (inference endpoints, vector databases, caching layers, and frontends) inside a single VPC, with GPU autoscaling that doesn't require a dedicated platform engineer to babysit.

How to use an SRE agent to reduce downtime

An alert in the middle of the night warns of a potential business failure. Manual incident response becomes more complex due to the overwhelming data from distributed and dynamic digital services. With an SRE agent, your engineering team can cut through alert clutter. They can sort through various signals quicker, decreasing burnout and achieving faster, more affordable resolutions. Operational resilience will see its next evolution with Agentic AI.

How Mezmo Uses Active Telemetry for Faster AI Root Cause Analysis

AI-powered root cause analysis only works when the data going into the model is clean, relevant, and structured. In this demo, we show how Mezmo's Active Telemetry approach helps engineers and SREs move from noisy application errors to immediate clarity. Using a restaurant ordering application running in Kubernetes, we trigger a database connection pool exhaustion issue and walk through two ways to investigate it with Mezmo.

See how Mezmo's AI Assistant instantly pinpoints root causes

This video shows how Mezmo's AI Assistant turns noisy telemetry into clear answers when errors spike. By preprocessing data and surfacing only the most relevant patterns, Mezmo quickly identifies issues like database connection failures or resource shortages and delivers actionable recommendations. Watch how AI-powered root cause analysis helps teams troubleshoot faster and with confidence. Mezmo's AI Assistant is built for platform engineers and SREs who need fast, reliable root cause analysis across high-volume telemetry pipelines — without manually sifting through noise.