Operations | Monitoring | ITSM | DevOps | Cloud

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.
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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.
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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.
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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.
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c1

Safeguarding Education in an AI-Powered World

Aug 20, 2026 By C1 In C1
Education is becoming more connected than ever across classrooms and campuses. Students learn through cloud platforms. Teachers use digital tools to collaborate and engage students. Institutions rely on connected systems to manage research, administration, communications, and campus safety. Technology is not just creating new possibilities for education, but it is also introducing new risks. Behind every device, application, and online account is a piece of valuable information.
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Komodor

Building AI SRE Agents, Part 2: Leave the Laptop, Earn Trust

Aug 20, 2026 By Nir Adler In Komodor
Moving the agent off your machine and pointing it at real clusters — read-only, in shadow mode — then climbing a trust ladder toward carefully scoped action. This is the second article in a three-part series on taking an AI SRE agent from a weekend experiment to enterprise production. Part 1 built a local agent on a throwaway cluster: read-only, propose-only, refined against a small eval set, with portable skills and no production write access.
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honeycomb

AI Norms & Values, Part 1 of 3: How We Do Business at Honeycomb

Aug 20, 2026 By Charity Majors In Honeycomb
It's been almost exactly one year since we issued our AI mandate here at Honeycomb, and we've been doing some reflection. When we issued our mandate, it's not like we hadn't been using AI. We were the first in the industry to bake a feature powered by AI into our product, way back in May of 2024. Many of us had been experimenting and using these tools in our spare time. But we believe that software is the killer app for AI.
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circleci

MCP vs API: How they work together and when to use each

Aug 20, 2026 By Jacob Schmitt In CircleCI
Summary: An API defines how software interacts with a service. MCP defines a standard way for AI applications to discover and invoke tools exposed by a service. They usually work together: an MCP server can sit in front of APIs you already run, turning low-level operations into capabilities an agent can find and use at runtime. Your API may already expose everything an AI agent needs. The harder problem is helping the agent figure out which operations matter for the task it has been given.
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jfrog

Don't Break the Agent: Lessons in Token Optimization

Aug 20, 2026 By Shay Dahan In JFrog
This one is for the curious souls who wonder how somebody actually builds a harness optimizer — and, more to the point, how they know it works. When we launched JFrog Boost into public preview, we told the story of the bill that broke us and the 100 billion tokens we clawed back across JFrog R&D. What that post didn’t cover is the question that consumed most of our engineering time: how do you measure any of this?
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gitkraken

You Aren't As Behind As You Think

Aug 20, 2026 By Chris Griffing In GitKraken
If you look at the people posting to social media the most about AI, you’ll probably feel left behind. They’re running dozens or hundreds of agents and probably shipping more than you. But, they are not representative of the rest of the dev world. They are the 1% of the 1%. The thing that might be missed is that you reading their posts is probably separating you from a much larger cohort of devs that are still trying to figure out how to use AI effectively in their day to day work.
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