Operations | Monitoring | ITSM | DevOps | Cloud

Introducing the next generation of the BigPanda AI Incident Assistant

Effective incident response depends on having all of the context surrounding what’s happening. You have to understand your systems, services, architecture, and teams deeply enough to correctly interpret whatever alert just fired. Too often, that context doesn’t arrive packaged neatly in one place. Gathering and interpreting context correctly under time pressure is one of the most difficult parts of the job.

Where AI Media Actually Slows Teams Down - And It Isn't Generation

The constraint on AI-generated video and imagery inside most organisations is no longer the model. It is the review loop, the consistency of a set, and a cost model nobody agreed on in advance - and none of those three get solved by switching to a better generator. In short: budget for iteration rather than render time; build a reference library before the first deliverable; define what a project's generation allowance is up front; and evaluate models on how they respond to a single prompt edit rather than on peak output quality.

How to Investigate a Production Incident Using an AI Agent (AppSignal MCP)

An incident has hit your product. I've been there: you're context-switching between hosting, CI/CD, codebase, AppSignal for monitoring, and whatever else your product depends on to minimize downtime and potential losses. You're trying to piece everything together, but it takes a lot of time, and that's something you don't have. AI agents connected to your tooling and your monitoring data via MCP free up that time for you.

Shared context for AI coding agents beats better tooling

The instinct when adopting AI coding agents is to optimize the agent. Compare models, tune prompts, argue about which editor has the better completion, and treat the agent as the thing that determines how fast the team moves. Then the commits go up and the product does not. The team building Upsun Dispatch took a different route, and the result is worth copying. They did not find a better agent.

AI Incident Response: Edwin AI in Slack Finds Root Cause Fast

AI incident response just got faster. Watch how LogicMonitor Edwin AI brings investigation, root cause analysis, and action directly into Slack for ITOps, SRE, DevOps, NOC, and incident response teams. When an incident hits, responders juggle monitoring tools, ITSM systems, dashboards, and documentation to find what they need. Edwin AI brings that context into Slack, so your team can investigate, decide, and act in one place.

Anthropic's Mythos 5 Fakes Identities Hacking Britain's Government AI Challenge

AI agents are now faking identities — and this is the case that proves it. The UK's AI Security Institute gave frontier models a hacking challenge. Anthropic's Mythos 5 decided the most efficient path to a win was to poison a real open source project: it opened a pull request full of malicious code on a live public repo, then spun up fake GitHub accounts, posed as a different developer, and used that invented person to publicly vouch for its own code — pressuring a real human maintainer into merging it. A human reviewer caught the malware and closed the PR.

Agent Mode Engaged! Enchaining Agentic Operations with Splunk AI Assistant 2.0

In this session, we will introduce your new "digital teammate"—the supercharged Splunk AI Assistant. We’ll demonstrate how the new Agent Mode provides the context, reasoning, and recommendations necessary to reduce your mean time to resolution (MTTR) from hours to minutes.

AI budgeting: how to plan and forecast AI spend

AI budgeting is the process of planning, allocating, and forecasting an organization's AI spend: model and API costs, AI infrastructure, tooling, and the people running it all. It differs from traditional budgeting because AI spend is usage-based, scales with product success rather than headcount, and often spans multiple providers.