Operations | Monitoring | ITSM | DevOps | Cloud

Optimize AI costs with Agent Observability

AI agent costs accumulate along more dimensions than a model-call count. Spend is a product of both which models you call and how the agent is architected: how many turns the loop runs, how many tools it invokes, how much retrieved context it carries, and how much of that context is re-sent on every turn. Provider bills tell you how much you spent, but they’re keyed to API keys and models instead of the applications and workflow steps that generated the calls.

Capping the AI free-for-all? Know what each dollar returns first.

Most companies gave everyone AI access and thought they’d sort out the costs later. Engineering, support, product, marketing, and finance all got licenses to the best models. The early results looked good, and for a while it didn’t matter there wasn’t an explicit business case. Let’s call the first half of 2026 the free-for-all period for many companies. AI adoption spread across the org fast, people and teams loved it, and then the invoices started coming in.

The Upsun Performance Agent: a diagnosis instead of a call graph

The data to explain a slow endpoint is usually already sitting in your project. Blackfire has the profile. Continuous profiling has the flame graph. The traffic numbers show which route matters. The code is in git. What takes the afternoon is putting those four things next to each other and working out which function is responsible. That afternoon is what the Upsun Performance Agent is for.

Security Just Hit Machine Speed

What if we could eliminate vulnerabilities before they become a problem? Neil Watkins from Burns & McDonnell explains how AI, automated security assessments, and Harness CI/CD capabilities could transform vulnerability management. From reducing attack surfaces to rapidly responding to critical zero-day vulnerabilities, the future of security is about moving faster without slowing down developers.

Sandbox Environments for AI Agents: A Full, Isolated Stack for Every Task

A code sandbox like Daytona or E2B isolates a process. A sandbox environment isolates a whole stack: services, databases, queues and configuration, cloned for one agent task and destroyed when it is done. Here is how sandbox environments work, what they are good for, and how to run them on Qovery. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

Policy as code for AI coding: How to turn engineering standards into guardrails for AI agents

Key Takeaways Engineering standards exist in every organization. They cover how services communicate, how secrets are managed, who reviews changes before they merge, and what "production-ready" means. The problem is where those standards live: wiki pages, onboarding docs, the memory of whoever happens to review a pull request that day. Conventional enforcement depended on reviewer knowledge, which varied across teams.

From iPhones to AI Servers: Foxconn's Second Act

The AI boom continues to redistribute revenue within the technology industry. If previously the main investment interest was focused on chip manufacturers and model developers, now companies that serve the infrastructure supporting this growth are becoming increasingly prominent. One of the main beneficiaries has been Foxconn, which is gradually transforming from the largest assembler of Apple devices into one of the key suppliers of server hardware for artificial intelligence.

How to Actually Test AI Agents Before Production

Fran Fernandez and Pramin Pradeep, co-founder and CEO of BotGauge AI, delve into how AI is changing software development, quality assurance, and enterprise IT. Pramin explains why faster AI-assisted coding has created new pressure on testing, why companies need visibility into every agent operating in their environment, and how red teaming, tracing, production observability, and human oversight can help organizations deploy agents safely.

AppSignal MCP Is Now a Claude Connector

AppSignal MCP went generally available last month. Until now, adding it to the Claude app meant pasting our endpoint URL into a custom connector form. Now you search for it like any other connector. Open Settings, go to Connectors, select the Discover tab, and search for AppSignal. That's the whole setup. For more detail on each step, follow the Claude app section of the setup guide. It works the same way in Claude on the web and in the desktop app..