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Swarm Investigation: Watch AI Agents Resolve Incidents in Minutes | BigPanda

Swarm investigation puts an entire team of AI agents on a major incident at once, cutting root cause discovery from hours to minutes. Every minute of a major incident costs real money, and traditional investigation, whether it's one engineer chasing a hypothesis or a team coordinating across tools, can't keep pace. This video shows how BigPanda's Swarm investigation dispatches AI agents to query systems, compare signals, and validate root cause in real time. See how autonomous incident response turns hours of manual troubleshooting into minutes.

How to Connect Cursor to CircleCI: AI-Powered CI/CD Debugging

Stop manually pushing branches, hunting for logs, and pasting errors back into your editor. This video shows you how to connect Cursor to CircleCI using the CircleCI CLI so your AI agent can trigger pipelines, read build output, and fix failures autonomously without you ever leaving the IDE. In this demo, we introduce a bug, let CI catch it, and watch the agent diagnose and fix it on its own, monitoring the pipeline until it comes back green. No tab-switching. No copy-pasting logs.

What Is AI Networking? The Two Pillars and Which One You Need

AI networking means two different things. Vendors rarely say which one they are selling. One is using AI to run the network you already have. The other is building network infrastructure fast enough to train AI models. Both pillars are real, and they solve completely different problems. Most network teams only ever need the first. In this blog, you will: You will finish knowing which pillar your own question belongs to.

Creating a private 5G network

Private 5G networks are dedicated cellular systems delivering ultra-reliable low-latency communication, massive IoT connectivity, and customized security for enterprises undergoing digital transformation. Private 5G subsumes advantages of both public and non-public networks, offering unified connectivity, optimized services, and customized security within a defined area.

AI Agent Infrastructure: Where to Run Agents in Production

Run production AI agents on infrastructure with hardware-level isolation, sub-second sandbox restarts, and a compliance posture that already covers PCI DSS, HIPAA, and GDPR, so a single misbehaving agent can’t touch another workload or your audit trail. Control Plane runs every agent workload in a Kata Containers sandbox on a per-workload Firecracker microVM, with Capacity AI packing resources and scaling workloads dynamically to cut compute cost 30-50%.

Introducing the Harness Connector for OpenAI: Bring software delivery into ChatGPT and Codex

Harness Connector for OpenAI: Bring CI/CD Context to ChatGPT & Codex A pipeline fails while you are working through a change in ChatGPT or Codex. To understand what happened, you need the execution details, the failed step, and the relevant pipeline configuration. Gathering that context can interrupt the work you were doing before you can even begin to solve the problem. The Harness Connector for OpenAI brings that delivery context into your AI workflow.

Cost per AI outcome: tying AI spend to results

Cost per AI outcome is your total attributed AI spend divided by the business results it produced: resolved tickets, converted leads, merged pull requests. It includes the cost of failed attempts, sits at the top of the AI unit-cost ladder, and it's the number that makes vendor outcome pricing, ROI claims, and build-versus-buy decisions comparable.

Before your AI bottleneck gets worse: what to put in place now

Your engineers have agents running. Not one agent, but several, spread across the team. Some run in a terminal on a laptop, some are wired into your CI jobs, and some live inside whatever coding tool each person prefers. Each one got set up separately, by whoever needed it, in whatever way worked that week. That is the state most teams are in right now. Code stopped being the slow part a while ago.

The Infrastructure Question Hiding Inside Every Government AI Conversation

Government AI strategies are ultimately constrained or enabled by the infrastructure beneath them. Agencies that can continuously validate controls, maintain visibility, and automate policy enforcement will be better positioned to deliver AI capabilities securely, responsibly, and at scale.