AI SRE
Root cause, auto-heal, and conversational ops in one layer
Finds the root cause within seconds, turns it into an approved runbook, rolls it out safely, and answers any question
The problem
Root cause means slow dashboard splunking and sequential troubleshooting, while the incident keeps burning.
How AI SRE solves it
- Finds root causes within seconds of an incident firing, reading logs, traces, metrics, change logs and code directly.
- Confidence thresholds act as an analytics quality gate on every reasoning.
- Every reasoning it builds feeds an incremental inductive memory library.
- Steering lets you edit and redirect the reasoning steps and re-execute against the same incident.
payments-api OOMKill cascade - 2026-07-04
payments-api · payments-worker
91%
Root cause found
Deploy a3f9c2 cut the payments-api memory limit from 512Mi to 256Mi - the container OOMKills under normal load.
Evidence
Reasoning steps
Deploy a3f9c2 lowered the payments-api memory limit from 512Mi to 256Mi
Kubernetes OOMKills the container repeatedly, producing a restart cascade across all 3 replicas
Review history
APPROVED by @priya · 14:31
The problem
The fix lives as tribal knowledge with one senior engineer, and the runbook that worked last time was never written down.
How AI SRE solves it
- Turns the identified root cause into a runbook and grows an incremental runbook library from every rollout.
- What one senior engineer knows becomes something the whole team executes.
- Shadow mode simulates against the live system with no write provision, before anything goes live.
- Tiered execution - auto-run, requires-approval, or always-manual - per service and per environment.
payments-api · OOMKill auto-remediation
Trigger · AI SRE diagnosis · confidence ≥ 85%
Execution mode
Actions
Revert deploy a3f9c2
restore memory limit 256Mi → 512Mi
Roll restart payments-api
3 replicas · surge 1, maxUnavailable 0
Verify recovery
OOMKill rate = 0 for 5 min
Post back to incident
attach actions to INC-4821
The problem
During an incident, engineers burn precious minutes jumping between dashboards, log viewers, and chat threads just to piece together what happened.
How AI SRE solves it
- Ask AI SRE in plain English - "What caused the latency spike on checkout at 3am?"
- It queries the RCA engine, Regen, and your observability stack for you.
- Returns one structured answer, sourced and cited.
- Approve a proposed action right from the same conversation.
Ask AI SRE
INC-4821 · payments-api OOMKill cascade
What caused the latency spike recently?
AI SRE
Deploy a3f9c2 cut the payments-api memory limit from 512Mi to 256Mi. The container OOMKilled under normal load, taking all 3 replicas into a restart cascade - p99 went 120ms → 2.4s.
Suggested
Features and Capabilities
Adaptive learning library
An incremental memory of every RCA and runbook it builds, so each new investigation and rollout starts smarter than the last.
Multi-source correlation
Correlates logs, traces, metrics, change logs and code in a single pass instead of checking one signal at a time.
Confidence-scored output
Every RCA ships with a numeric confidence score and the exact evidence trail behind it, not just a guess.
Shadow mode
Simulation against the live system with no write provision. Runbooks and playbooks move to live only once approved.
Tiered execution
Auto-run, requires-approval, or always-manual - you decide which tier each action type falls into, per service and per environment.
Conversational routing & source-cited answers
Ask in plain English from Slack, Teams, or the console. Every answer links back to the exact RCA, runbook, or incident timeline entry it was pulled from.
Human in the loop
Every RCA and runbook reasoning undergoes a quality check with manual approval before it enters the library.
Redaction
PII and secure information masking, including email, name, IP and phone number, to avoid any data leakage to the LLM.
Guardrails
Hard limits on reasoning loops, tool-use depth and iterations, token usage and LLM response bounds and much more.
Evaluation benchmarks
Accuracy gates with confidence scores - 80% reasoning accuracy when all reasoning prerequisites are provided.
Sample Walkthrough
Signal ingestion
The moment an alert fires, AI SRE reads logs, traces, metrics, change logs and code directly - no engineer has to open a single dashboard first.
Root cause, scored
Anomalies across every signal are cross-correlated into a ranked, confidence-scored root-cause hypothesis, citing the exact log lines, traces and commits behind it.
Runbook reasoning
The confidence-scored diagnosis becomes a step-by-step remediation plan, AI-assisted or pulled from the incremental runbook library that grows with every prior rollout.
Shadow simulation
Every step dry-runs against the live system with no write provision first, logging the predicted diff before anything is touched for real.
Approval gate
The runbook is proposed in Slack or Teams with one-click approve/reject - nothing above the auto-run tier executes without a human sign-off.
Live execution & verification
Approved actions run for real, then AI SRE verifies recovery and logs the outcome back to the incident timeline Regen is tracking.
Ask, any time
At any point in the sequence, ask what happened in plain English and get one structured, source-cited answer - no dashboard-hunting required.
How accurate is AI SRE
80%
Accurate
Integrated with the production tools.
Human in the loop
Every RCA and runbook reasoning undergoes Quality check with manual approval before it enters the library.
Steering capability
Edit and redirect the reasoning and tool-usage steps, then re-execute to sharpen the result.
Guardrails
Hard limits on reasoning loops, tool-use depth and iterations, token usage and LLM response bounds.
Redaction
Sensitive information masking. Strict data governance preventing data leakage to LLM.
Evaluation benchmarks
Accuracy gates with confidence scores and validation benchmarks.
Shadow mode
Simulate against the live system with no write provision. Runbooks and playbooks move to live only once approved.
Built to be trusted with production
Data Leakage Prevention
Sensitive information masking with strict data governance layers.
Multiple Deployment Options
On-prem, Air-gapped and Private Cloud, Managed Cloud SaaS Deployments.
Access Control and Auditing
RBAC, MFA, Custom SSO/SCIM provisioning and Audit trails
Compliant on
Deploys to
AI SRE Suite
Where does it sit in the Suite?
Watch the fix unfold, from your pocket.
Every action AI SRE takes shows up live in the FluidifyAI app - status, evidence and outcome - the moment it happens. No laptop, no dashboard hunting. Just open the app and watch the resolution the way you'd watch a match.
Live status feed - every action moves from pending to verified in real time
No laptop required - follow the resolution from your phone, anywhere
Push notified the moment your approval is needed
FluidifyAICRITICAL · INC-4821
payments-api OOMKill cascade
Revert deploy a3f9c2
restore memory limit 256Mi → 512Mi
Roll restart payments-api
3 replicas · surge 1, maxUnavailable 0
Verify recovery
OOMKill rate = 0 for 5 min
Post back to incident
attach actions to INC-4821
Frequently asked questions
AI SRE ships with the Enterprise plan alongside Regen, on a custom annual contract with the complete integration stack and extended reasoning with steering mode.