On a recent Saturday in Berkeley, I sat through about a dozen talks on Agentic AI. The one that really fired me up was from Louie.ai, a company building AI copilots for Incident Coordination.

I use AI coding tools regularly for day-to-day tasks, but I had not seriously considered how similar technology might transform Incident Management. As someone who rotates into the Incident Coordinator role and has seen my share of messy, high-pressure incidents, the idea that an AI system could take some of the load off felt both obvious and inspiring.

Reflecting on the talks for a bit, I wanted to sketch the space more clearly and think about where this technology is headed.

Who’s playing in this space?

The Louie.ai talk introduced me to the term “Vibe Investigation.” Their system can summarize alerts, draft containment steps, and keep stakeholders updated autonomously. That got me curious about who else is building for this space:

• Microsoft Security Copilot integrates with Defender and Sentinel to suggest investigation and response paths.
• Google and Mandiant with Gemini are experimenting with layering large models into their incident response services.
• Palo Alto Cortex XSIAM has been moving toward what it calls an “autonomous SOC,” which is now looking more like an agent-based system.
• ServiceNow Security Incident Response is starting to include generative copilots for coordination and documentation.
• A handful of early-stage startups like Louie are also emerging, building copilots aimed at coordination, documentation, and simulation (for example 7ai and Edwin AI from LogicMonitor).

Tnis quick list I was able to pull together is a clear signal to me that AI tools for incident coordination are becoming their own recognized category.

Some obvious opportunities

Incidents carry real costs. A delayed response can mean customer downtime, revenue impact, or regulatory exposure. Yet there is always tension: teams want to escalate quickly, but teams and leaders may pushe to minimize the number of Incidents, creating hesitation to use the system. These tools could help massively by lowering the friction and speeding up the path from signal to resolution.

Here are a few areas where automation could make an immediate difference:

• Lowering barriers to escalation: today, a human usually has to decide whether to raise an Incident, and that decision is often ambiguous. An AI assistant could spot patterns earlier and suggest escalation with evidence to back it up.
• Faster assembly of the right team: getting the right people on the call is often trivial, but sometimes you waste cycles pulling in the wrong folks. An AI system could recommend the right responders based on historical incident patterns.
• Streamlining root cause analysis: RCA is almost always the slowest and most draining part of an Incident. Multiple environments and deployments make it worse. AI could connect the dots across logs, code changes, and telemetry to narrow down the possible causes.
• Pattern matching against code changes: I have seen Incidents recur because a quick fix was shipped under pressure, only applied in certain environments, or repeated by mistake. AI could automatically compare new fixes against past Incidents and flag likely regressions.

What still feels risky

As an Incident Coordinator, I would love an AI tool that reduced the pain and confusion of managing Incidents. That said, I can also appreciate how the complexities of different architectures would cause products to have long implementation timelines. These are real risks that any serious tool will need to solve:

• Opaque reasoning: recommendations are sometimes delivered without showing why. In regulated industries like finance and healthcare, clear reasoning will be a requirement.
• Governance traps: to fully triage and understand an Incident, agents may require permission to access sensitive data or environments. There need to be strong access management controls and opt-out abilities for customers who do not want sensitive data viewed in the name of Incident resolution.
• Continuity gaps: Incidents can span many environments and run for long time frames. Agents may not yet support the level of context needed to be truly helpful.

Some closing thoughts

AI is not going to replace the Incident Management team any time soon, but it is already showing promise in reducing a lot of the pain. For me, the biggest takeaway from the RDI summit was realizing how mature this space has already become. I left energized, imagining the opportunity to bring a tool like this to my team in the future, hopefully not too distant.