Operations | Monitoring | ITSM | DevOps | Cloud

Every IT leader is under pressure to show AI results. Budgets are approved, pilots are launched, and vendors promise transformation within a quarter. Some teams are already running AI agents in production, resolving tickets and answering employees without human intervention. Others are still stuck in proof-of-concept purgatory, six months into a rollout with nothing to show a board. The thing is, AI doesn't fix what's broken in an IT operation, it multiplies what's already there.

GitBench is our attempt to see how well LLMs handle git-specific tasks. We put together a suite of 17 benchmark categories with a total of 204 specific test cases. We ran these benchmarks against 43 (and counting) different models with all of their supported effort/reasoning levels.

There’s a difference between seeing a problem and preventing one is not a question of tooling. It is a question of operational posture. Across eleven operator interviews at Nexus Live, a consistent pattern emerged. Teams are not struggling because they lack visibility. They are struggling because visibility alone does not produce confidence. Alert floods, late root cause discovery, and 3am escalations have become normalized in hybrid environments. The result is not just fatigue.

Every time a client connects to your server, a small negotiation happens before a single byte of application data moves. That negotiation, the TLS handshake, is what makes encrypted web traffic possible. It's also at risk of being recorded. Not the content of your sessions, but the handshake itself. And that's enough. A well-resourced attacker doesn't need to break your encryption today.

Agentic AI has moved quickly from experimentation to enterprise planning. IT leaders are no longer asking whether AI agents can summarize tickets; they’re asking a more important question: Can agentic AI actually complete work consistently and measurably? That is where agentic process automation becomes critical.

Not all alerts are created equal. Some are resolved quickly by the on-call engineer. Others signal something serious enough to affect your business and require your whole team to coordinate. That is why we redesigned incidents as a dedicated coordination workspace for the alerts that have the most business impact.‍ Until now, incidents in ilert were used to communicate status updates to customers and stakeholders. Creating one meant publishing to your status page. We have separated the two.

DASH 2026 brought thousands of engineers, builders, security professionals, and technology leaders to New York City for 2½ days focused on building, operating, and securing modern systems. Across hands-on sessions and more than 40 customer talks, teams shared how they’re tackling real-world challenges at scale with Datadog. On stage, the keynote set the direction for what’s next across observability, security, and AI, highlighting a shift toward more autonomous, AI-assisted operations.

A digital transformation can help a business thrive. Developing a strong technology stack that works for your business is essential for growth. Investing in a digital transformation means finding what works for your business and ensuring the best operations through your digital tools.

This is Post 2 of The Governance Gap series. Post 1, "The New Software Creator," established that the most significant shift from AI isn't developer speed - it's that the population of builders has fundamentally expanded. Something quiet happened in most engineering organizations over the last 18 months.

There is a question a lot of engineering leaders are quietly sitting with right now: we have rolled out AI tools across the team, the developers seem faster, so why isn't more software actually shipping? It is a reasonable thing to consider. Pull requests are opening faster. Lines of code per sprint are up. The boilerplate that used to take full afternoons now takes minutes. By every local measure, the investment is paying off.