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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.

What's new from BigPanda: September 2026 Product Updates

Most teams we talk to are fighting the same battle. The knowledge needed to make a decision already exists somewhere. However, it’s locked in a tool your team isn’t looking at, or in the head of the one engineer who’s seen this same type of incident before. This month’s updates all chip away at that same problem. Here’s what’s new.

The enterprise changed. ITOps didn't.

The modern enterprise runs on a technology stack that changes faster than the operating model responsible for keeping it available. Applications that once moved through scheduled releases now change continuously. Infrastructure is distributed and dynamic. Services depend on other services, teams depend on other teams, and operational data arrives from more places than any individual can reasonably inspect. The business asked for agility, flexibility, and velocity, and the tech delivered.

Introducing Swarm Investigation from BigPanda: Autonomous, multi-agent IT incident investigation

When a major incident opens, the opening minutes often become a race across disconnected tools and competing theories. One engineer checks a monitoring tool. Another scrolls through change records, looking for the one line that explains everything. A third pings Slack, asking if anyone has seen this before. While these are reasonable steps, taken one at a time, in sequence, they are far too slow.