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How we built Grafana Assistant - a conversation about AI development for observability

Feb 6, 2026 By Grafana In Grafana
This conversation with Grafana Labs engineers, Mat Ryer, Cyril Tovena and Sven Großmann, dives deep into the engineering behind Grafana Assistant, exploring how agentic AI is transforming the observability landscape. From hackathon origins to sophisticated backend agents, the team shares candid lessons on building, scaling, and refining AI tools for engineers.
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How AI is democratizing video and what it means for your brand

How AI is democratizing video and what it means for your brand

Feb 6, 2026 By OpsMatters In OpsMatters
Video stopped being optional years ago. In 2026, 95% of marketers say video increases brand awareness, and 60% report it directly drives sales. But for small businesses and solo entrepreneurs, there's always been a gap between knowing video matters and actually making it. The costs, the learning curve, the time-it adds up fast.
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Operational Risks and Controls When Deploying Legal AI

Operational Risks and Controls When Deploying Legal AI

Feb 6, 2026 By OpsMatters In OpsMatters
A law firm recently found that its AI tool had misread "limitation of liability" clauses for 6 months. No one noticed the mistake. The error only came to light when a client faced a huge insurance claim that the firm had promised was capped. The cost? That firm is now dealing with a malpractice lawsuit and a damaged reputation. Using AI in a law office poses risks beyond simple computer bugs. These tools mix technical errors with professional responsibility. As AI becomes a standard part of the job, firms without strict rules will face quality issues and legal trouble.
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iOPEX

Voice AI: The Missing Link in Your Agentforce Strategy

Feb 5, 2026 By iOPEX Technologies , In iOPEX
Despite the enterprise-wide pivot toward digital deflection, voice remains the primary escalation channel for high-complexity customer issues. Yet, while organizations rigorously optimize digital touchpoints, telephony frequently remains a siloed legacy endpoint, disconnected from the broader CRM architecture. This integration gap creates a strategic blind spot that fundamentally undermines your digital roadmap.
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solarwinds

The Human-Centric Stack: Why Logs Are the Great Equalizer in the Age of AI

Feb 5, 2026 By Rachel Revoy In SolarWinds
In 2026, we are seeing incredible feats of engineering with agentic AI, impacting metrics and distributed traces that map thousands of microservices. Our systems have never been more intelligent and complex. However, as our observability becomes more intelligent, fewer employees know how to manage and troubleshoot complex systems. These employees, who often bear the brunt of an error’s impact, may need to rely on specialists to interpret the system.
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lightrun

Kiro Can Now Reason With Lightrun's Live Runtime Context

Feb 5, 2026 By Gideon Freud In Lightrun
AI code generation is fast. Making it reliable requires runtime context. Today, Kiro gains live runtime visibility with the Lightrun MCP. This grounds AI-assisted development in how code actually behaves at runtime. Kiro, the AI coding assistant from the teams at AWS, is built for velocity and intuition. It moves from specification to production with speed and structure, helping teams turn intent into working code. But until now, like every AI coding assistant, Kiro had a major blind spot.
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honeycomb

How Honeycomb Supercharges OpenTelemetry for AI

Feb 5, 2026 By Fahim Zaman In Honeycomb
It has become common knowledge that the nature of software development has changed as AI-code generation and agent-based features gain adoption. In perhaps a more subtle shift, the fundamentals of software instrumentation are changing too. As OpenTelemetry becomes the standard instrumentation layer across enterprises, with thousands of developers (many from Honeycomb) actively contributing to it, the nature of the telemetry data captured itself is evolving to meet the growing demand for rich context.
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Komodor

The AI-Empowered Site Reliability Engineer: Automating the Balance of Risk and Velocity

Feb 5, 2026 By Udi Hofesh In Komodor
You might expect an AI-SRE agent to target 100% reliable services, ones that never fail. It turns out that past a certain point, however, increasing reliability is worse for a service (and its users) rather than better! Extreme reliability comes at a non-linear cost: maximizing stability limits how fast new features can be developed, dramatically increases the operational cost, and reduces the features a team can afford to offer.
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