# Self-learning based incident response management.

* Incident management and response is one of the few genuine problems we have encountered personally over the past 3 years.
* DB crashes, instant fallouts, attacks, unexpected code bugs that are trigerred due to user interactions.
* The SLAPA [demo](https://twitter.com/DYtweetshere/status/1631349179934203904) was posted on twitter a few days back, and is evidently a simple but powerful example of self-learning agents built upon language models.
* Pagerduty and several others have demonstrated the prudence in incident management, reporting and responses as a category.
* Self-learning agents, in themselves, are an incredibly experimental technology that is nascent and full of issues. But, it's a natural progression of how models and software will work together in the future.
* SLA is a technology feat (hard to achieve, but definitely valuable), especially when applied to incident responses.

#### Product Scope
* SLA can be applied to incident management and responses in human-in-the-loop and fully automated ways.
* SLA is implemented by creating an agent, definining it's incident response goal (eg: X API should always be up and return {"key":"value"}), and then giving the agent a scope of permissions it's allowed to access while solving the incident (browse the internet, access my database, access my github repos.etc)
* Once SLA identifies recourse steps, it's either sent as step-by-step clicks for a human DevOps/SecOps engineer to implement or is auto-implemented.
* Since SLA can reference off of a centralized database, it gets smarter and non-repetitive as more incidents are solved.