DeepOps — Autonomous SRE Platform

An AI-powered autonomous cloud operations platform that behaves like an experienced SRE team.

Status: Early prototype — active development.

Vision

Instead of merely monitoring infrastructure, DeepOps continuously:

• Deploys infrastructure from natural language
• Monitors health across metrics, logs, traces, and events
• Detects anomalies before users notice
• Finds root causes through causal chain analysis
• Repairs services automatically (with human approval for high-risk actions)
• Learns from every incident to improve future responses
• Optimizes cost across cloud resources
• Prevents future failures by recognizing known patterns

The long-term goal: An operating system for cloud infrastructure.

Architecture

                         ┌──────────────────────┐
                         │    Planner Agent      │
                         │  (intent detection)   │
                         └──────────┬───────────┘
                                    │
                         ┌──────────▼───────────┐
                         │     Orchestrator      │
                         │  (agent coordinator)  │
                         └──┬──┬──┬──┬──┬──┬──┬─┘
                            │  │  │  │  │  │  │
              ┌─────────────┘  │  │  │  │  │  └──────────────┐
              │                │  │  │  │  └────────────────┐ │
         ┌────▼────┐    ┌─────▼──▼──▼──▼──┐    ┌───────────▼─▼──┐
         │Infra    │    │  Observability  │    │    Incidents   │
         │Provision│    │  Monitor        │    │    Detector    │
         │Scaler   │    │  Pipeline       │    │    Root Cause  │
         └─────────┘    └─────────────────┘    │    Remediator  │
                                               │    Responder   │
         ┌─────────┐    ┌─────────────────┐    └────────────────┘
         │Security │    │  Cost           │
         │Scanner  │    │  Optimizer      │    ┌────────────────┐
         └─────────┘    └─────────────────┘    │  Learning      │
                                               │  Engine        │
         ┌─────────┐    ┌─────────────────┐    │  (Memory)      │
         │Backup   │    │  Updates        │    └────────────────┘
         │Manager  │    │  Patcher        │
         └─────────┘    └─────────────────┘

Current Modules

Module	Status	Description
Core Orchestrator	✅ Basic	Agent coordination and task dispatch
Planner Agent	✅ Basic	Natural language intent detection
Learning Engine	✅ Basic	Incident memory and pattern recall
Infrastructure	⚡ WIP	Provisioner analyzes app stacks and recommends infra
Auto Scaler	⚡ WIP	Predictive scaling decisions
Monitoring	⚡ WIP	Metrics, health checks, observability
Pipeline	⚡ WIP	Observability data ingestion
Anomaly Detector	⚡ WIP	Signal analysis and alerting
Root Cause Analyzer	✅ Basic	Causal chain construction
Remediator	✅ Basic	Remediation action planning
Incident Responder	⚡ WIP	Full incident lifecycle
Security Scanner	⚡ WIP	Vulnerability, secret, and IAM scanning
Cost Optimizer	⚡ WIP	Waste detection and optimization
Backup Manager	⚡ WIP	Backup and disaster recovery
Patcher	⚡ WIP	Automated update planning

Quick Start

python -m deepops

Or install and run:

pip install -e .
deepops

Usage

Run in interactive mode:

python -m deepops

Then type natural language commands:

deepops> Deploy my MERN app
deepops> Why is latency increasing?
deepops> Fix production
deepops> Optimize my AWS bill
deepops> Scale the API
deepops> Scan for vulnerabilities
deepops> status
deepops> help

Or pass a command directly:

python -m deepops "Why is latency increasing?"

Examples

Root Cause Analysis

Input:  Why is latency increasing?
Output: Causal chain:
  1. API latency increased to 280ms p99
  2. Redis query timeout at 200ms
  3. Node.js event loop blocked
  4. High disk IOPS on EBS volume
  5. EBS gp2 volume IOPS exhausted
  6. Root cause: Recent deployment v2.3.1 changed cache TTL

Cost Optimization

Input:  Optimize my AWS bill
Output: Identified $2,847/month waste:
  - Idle instances: $86/month
  - Oversized DB: $432/month
  - Unattached volumes: $50/month
  - Overprovisioned K8s nodes: $1,200/month

Incident Response

Input:  Fix the memory leak
Output: Plan:
  1. Restart affected container (medium risk)
  2. Increase memory limit 512Mi → 1Gi
  3. Profile heap for root cause
  4. Redeploy with fix

Project Status

This is a rudimentary prototype being built from the ground up. Current focus areas:

• Connect modules into a working pipeline
• Add persistent memory for incident learning
• Build the autonomous remediation workflow
• Add predictive scaling based on real patterns
• Integrate with actual cloud providers (AWS SDK)
• Real observability pipeline (OpenTelemetry)
• Web UI for dashboards and incident timelines
• Multi-agent coordination and arbitration

Long-Term Vision

A cloud platform that continuously observes its own state, predicts failures before users notice them, repairs itself safely, remembers what worked, and improves after every incident — reducing the need for manual SRE intervention while keeping humans in control for high-risk operations.

---

Built with the belief that infrastructure should adapt to applications, not the other way around.