/AquanowAI SRE Agent
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Active engagement — Jan 2026 to Present Production issues diagnosed in minutes.
Not hours.
An autonomous AI SRE agent that connects to six production systems — Jira, Confluence, Slack, GitHub, Coralogix, and AWS — to debug incidents without human triage. MTTR reduced from 30 minutes to 5.
sre-agent — incident triage ▋
6-person SRE team. Manual triage. Thousands of hours lost.
When an alert fires at 3 AM, an engineer spends 30 minutes gathering context across six different systems before the actual diagnosis even begins. The AI SRE agent does this in under 60 seconds.
Manual SRE (Before)
Engineer woken at 3 AM, 30 min gathering context
Checks 6 different dashboards manually
Root cause found by pattern-matching from memory
RCA written days later (if at all)
Same issue hits again — no institutional memory
6-person SRE team handling alert volume
AI SRE Agent (After)
Full context gathered in under 60 seconds
All systems queried in parallel, correlated
Root cause from logs + code + past incidents
RCA drafted in real-time with evidence links
Past incidents indexed and referenced automatically
Agent handles triage, humans approve fixes
Detect. Investigate. Diagnose. Resolve.
An autonomous incident intelligence pipeline — from alert to root cause to resolution, with human approval before any production changes.
01
Detect
Alert fires — PagerDuty, Coralogix, or Slack. Agent immediately ingests the signal and begins autonomous triage.
02
Investigate
Pulls context from Jira, Confluence, Slack threads, GitHub commits, and Coralogix logs. Correlates timelines across systems.
03
Diagnose
Identifies root cause using log patterns, recent code changes, infra state. Cross-references past incidents for known failure modes.
04
Resolve
Generates fix recommendation with evidence. Drafts Jira ticket, posts RCA to Slack, suggests remediation steps — human approves.
Detect→Investigate→Diagnose→Resolve
Six systems. One agent. Real-time correlation.
The agent queries all production systems in parallel, building a unified incident timeline in seconds — work that previously required an engineer to context-switch between dashboards.
Jira
Issue tracking — reads tickets, creates RCA reports, links related incidents
Connected
Confluence
Knowledge base — searches runbooks, architecture docs, past postmortems
Connected
Slack
Team comms — reads incident channels, posts diagnostics, alerts on-call
Connected
GitHub
Source code — correlates recent commits, reviews PR diffs, checks deploy history
Connected
Coralogix
Observability — queries logs, traces, metrics. Identifies error spikes and anomalies
Connected
AWS CLI
Infrastructure — checks EC2 state, ECS tasks, CloudWatch alarms, RDS health
Connected
What the agent actually does.
Not a chatbot. An autonomous system that investigates, correlates, and diagnoses — then waits for human approval before acting.
01
Multi-System Correlation
Simultaneously queries Jira, Confluence, Slack, GitHub, Coralogix, and AWS. Builds a unified incident timeline across all systems in seconds.
02
Historical Pattern Matching
Indexes past incidents, postmortems, and resolutions. When a new alert fires, cross-references against known failure modes before starting from scratch.
03
Code-Aware Diagnosis
Pulls recent commits and PR diffs from GitHub. Identifies if a recent deployment correlates with the error pattern — the most common root cause.
04
Log Intelligence
Queries Coralogix with targeted searches. Identifies error spikes, traces request paths, and extracts stack traces that point to the failure.
05
Autonomous RCA Generation
Produces a structured root cause analysis with evidence from every system. Links to relevant logs, commits, and past incidents. Posts to Jira and Slack.
06
Human-in-the-Loop Resolution
Agent diagnoses and recommends. Human reviews and approves. No autonomous production changes — safety first.
Built for production. Deployed since January 2026.
stack: Python + FastAPI + Claude API
integrations: Jira REST API, Confluence REST API, Slack Web API, GitHub REST API, Coralogix API, AWS CLI/SDK
pattern: Event-driven — alert webhook triggers autonomous investigation pipeline
safety: Human-in-the-loop for all resolution actions. Agent diagnoses, human approves.
deployed: Production since January 2026
Built by SapienEx
Your production issues, diagnosed autonomously.
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