Operations | Monitoring | ITSM | DevOps | Cloud

When to Use Grafana Assistant vs. MCP vs. gcx: Part 3

Aug 21, 2026 By Grafana In Grafana
When should you use gcx? If Grafana Assistant is the brain and Grafana MCP is the easy hand, gcx is the power hand. Built for AI agents working in the terminal, gcx gives them deep access across Grafana Cloud—so they can pull telemetry, verify code, automate workflows, and access places MCP doesn’t. Coding agents? gcx. Need the full Grafana Cloud surface? gcx. Automating in CI/CD? gcx. Here’s where it fits, and when to use it — explained by Nicole van der Hoeven.
View Video
lightrun

Why AI Agent Orchestration Needs Runtime Context Between Agents

Aug 21, 2026 By Lightrun Team In Lightrun
Every multi-agent system depends on one agent handing its output to the next, and nothing in the architecture confirms that the handoff carried what it should have. Orchestration adds a failure surface that single-agent architecture doesn’t have: a point between every two agents where one has to trust that the other passed along everything it needed, unverified.
Read Post

Run Playwright Tests with Harness AI Test Automation | CI/CD + AI Failure Analysis

Aug 21, 2026 By Harness In Harness
End-to-end testing should not slow your delivery pipeline down. But for many teams, Playwright test suites still live in disconnected jobs, produce noisy failures, and make it hard to tell whether a failed test is a real regression, a flaky test, or just another false positive. In this walkthrough, Shibam Dhar, DevRel Engineer at Harness, shows how Harness AI Test Automation helps teams run, analyse, and trigger Playwright tests directly from their software delivery workflows.
View Video
Dataset Bias in Computer Vision: How to Audit Human Image Data

Dataset Bias in Computer Vision: How to Audit Human Image Data

Aug 21, 2026 By OpsMatters In OpsMatters
Dataset bias in computer vision cannot be evaluated from one demographic percentage. The distribution available to a model is shaped by where images came from, how subjects entered the collection, which examples were retained, how labels were defined, what visual conditions were represented and how evaluation data was constructed. A useful dataset bias audit therefore examines the complete data pipeline.
Read Post
How AI Is Turning Job Search Into an End-to-End Digital Workflow

How AI Is Turning Job Search Into an End-to-End Digital Workflow

Aug 21, 2026 By OpsMatters In OpsMatters
Job searching used to be a loose collection of tasks: a few bookmarked roles, a resume file named "final_FINAL," and a handful of half-finished applications spread across tabs. Now, it's starting to look more like an actual workflow-one with inputs, outputs, checkpoints, and iteration. That shift is partly driven by AI tools that help candidates move from "I should apply" to "application sent" with fewer dropped steps. Platforms like ResumeCoach are part of that wider trend: not just creating documents, but helping people build a repeatable process they can run again and again.
Read Post
upsun

How task containers give AI agents real infrastructure without idle cost

Aug 20, 2026 By Upsun In Upsun
Infrastructure for AI agents usually forces a choice between two bad options. A sandbox is safe but blind, cut off from the data and services that would make the agent's output useful. Full access means paying to keep a container idle between runs, waiting on a prompt that might not arrive for hours. Task containers, which Upsun released on August 12, 2026, are built to avoid that choice. A task container is a single-purpose container defined in a project's.upsun/config.yaml file.
Read Post

Why AURA Scratchpad Is Rad: Bound the AI SRE Agent Context Window

Aug 20, 2026 By Mezmo In Mezmo
A big tool result does not have to be a big context cost. AURA moves it to disk and hands the model a pointer plus the tools to navigate what is there. A large MCP tool result can consume or overflow an agent's context window, and on a third-party server you do not control how much comes back. Scratchpad breaks the link between how big a tool result is and how much context it costs: the full output goes to disk, and only the slice the model asks for ever enters the window. Errors always pass through inline, so the model can react to them.
View Video
lightrun

Why AI Agent Architecture Needs a Runtime Context Layer

Aug 20, 2026 By Lightrun Team In Lightrun
Every AI agent architecture diagram shows the same five layers: perception, memory, reasoning, action, and feedback. Each layer assumes the one before it worked correctly, and none of them can confirm that once the agent runs against live production data. Runtime context is the sixth layer most designs leave out, and it’s the one that decides whether any of the other five can be trusted.
Read Post
gitkraken

Everyone Feels Faster. Almost Nobody Can Prove It.

Aug 20, 2026 By Jade Nangah In GitKraken
What 554 developers and engineering leaders told us about AI, agents, and the measurement gap nobody’s closing. Ask a developer if AI made them faster this year, and 84% will say yes. Ask their VP to put a number on it for the board, and 39% will have nothing to show. That gap, not adoption, is the real story in engineering right now.
Read Post

Redgate Flyway Enterprise MCP for AI and Agentic Workflows

Aug 20, 2026 By Redgate Software In Redgate
Huxley Kendell demonstrates how Redgate Flyway Enterprise enables safe, governed, and deterministic integration of AI and agentic workflows into database development processes. Local Workflows Huxley showcases how developers can use an MCP server to interact with Flyway Enterprise through local AI tools like Claude. Agentic Workflows The demo presents a future-ready, fully agentic workflow designed for enterprise automation through an autonomous copilot.
View Video

Copyright © 2026 OpsMatters™. All rights reserved.