Operations | Monitoring | ITSM | DevOps | Cloud

The AI trust dial: from local agents to autonomous software factory

There are many conversations about the use of AI, particularly how engineering teams are using it in their coding workflows. Manual work is being replaced by agent-driven automation, and human value increasingly lies in the higher-order work: writing specs, thinking through architecture, steering the direction, exercising taste, and reviewing the output.

You can't audit an AI model the way you audit a binary

Open up an AI model and what's actually inside is a floating array of decimal points. No one can look at that and confirm it hasn't been tampered with, doesn't contain bias, or wasn't trained on poisoned data. This video covers why that changes how you need to think about trusting a model: If you can't unpick the model itself, you have to be able to trust its origin.

Top 6 Multi-Agent Orchestration Tools for Software Teams

Software teams have already seen what single-agent tools can do. They can draft code, explain unfamiliar functions, summarize pull requests, generate tests, and clean up documentation. Those tasks are useful, but they do not solve the larger coordination problem that slows down engineering work.

5 AI Tools Cutting SaaS Costs for IT and Marketing Teams in 2026

SaaS sprawl has become one of the quieter budget problems inside IT and marketing departments. Every team picks up a new tool to solve an immediate problem, nobody audits the stack regularly, and eighteen months later finance is asking why the software budget has ballooned while adoption of half those tools sits in single digits. AI tooling has followed the exact same pattern over the past two years, arguably faster than any other category before it.

Best Voice AI Orchestration Platforms in 2026

Building an AI voice agent can be easy, but making it actually work on real phone calls is more challenging. Voice AI orchestration platforms help to connect speech-to-text, text-to-speech, and telephony networks into real-time conversational agents. Although these tools are AI-powered, they handle turn-taking and interruptions during web interactions. By using these platforms, you will get fluid voice conversations that are ready to use.

How AI Answer Engines Like Perplexity Choose Which Brands to Cite in 2026

More product research now starts inside an AI assistant instead of a search engine. When someone asks ChatGPT, Perplexity or Google's AI Overviews for the best option in a category, they get a short written answer that names a few brands and links to a handful of sources. The brands that are named win the attention. The rest are not shown at all.

Why AI Adoption Fails Without Operational Maturity First

Most MSPs are already experimenting with AI in some form, and every vendor at every conference has an AI for MSPs pitch ready. Few have stopped to check whether their own operations are solid enough to scale. Our 2026 IT Trends Report found that three-quarters of IT leaders believe they have an AI policy, while fewer than half of help desk staff agree. That’s the real risk: AI doesn’t fix drift, unclear ownership, or gaps between what’s documented and what’s actually happening.

AI speeds up delivery. Here's how IT leaders manage the risk when AI-generated code hits production.

AI can accelerate speed to market, but for IT leaders it also raises a harder question: can you prove how an AI-generated change reached production? Chris Yates (SVP, Managing Director of Data & Architecture, Republic Bank) explains how his team builds a full evidence trail for every change, using version-controlled deployment tooling like Redgate Flyway Enterprise, so governance becomes a guardrail rather than a brake on speed.