A decentralized, event-driven agentic LLM architecture orchestrating end-to-end DevOps automation, SRE, and infrastructure cost optimization at an enterprise scale.
Traditional pipeline automation and alerting mechanisms are fundamentally broken—plagued by alert fatigue, context switching, and reactive firefighting. AutoOps introduces a paradigm shift: replacing static CI/CD pipelines and manual SRE interventions with an autonomous swarm of highly specialized AI agents.
Engineered by Ismail Sajid, this system leverages advanced agent-oriented programming (AOP) through LangGraph and CrewAI to deliver Zero-Defect Deployments, Automated Root Cause Analysis (RCA), and Self-Healing Infrastructure without waking up your core engineering team at 3:00 AM.
Built on top of Anthropic's Claude 3.5 Sonnet and OpenAI's GPT-4o, we orchestrate a state machine where PR reviews, CI diagnostics, and P1 incidents are evaluated with the precision of a 50-year veteran Principal Engineer.
The core intelligence is driven by a decentralized multi-agent system (MAS). We utilize an event-driven router (FastAPI) feeding into a Directed Acyclic Graph (DAG) state machine managed by LangGraph.
graph TD
classDef event fill:#8A2BE2,stroke:#333,stroke-width:2px,color:#fff;
classDef agent fill:#008080,stroke:#333,stroke-width:2px,color:#fff;
classDef output fill:#B8860B,stroke:#333,stroke-width:2px,color:#fff;
A[Webhook Telemetry]:::event -->|API Gateway| B(FastAPI Router)
B -->|State Transition| C{LangGraph Event DAG}:::event
C -->|Trigger: pr_opened| D[CodeReview Agent]:::agent
C -->|Trigger: ci_failed| E[CI Monitor Agent]:::agent
C -->|Cron: auto_scale| F[Infra Optimizer Agent]:::agent
C -->|Trigger: pagerduty| G[Incident Responder]:::agent
C -->|Trigger: commit| H[Documentation Agent]:::agent
C -->|Cron: sec_scan| I[Security Audit Agent]:::agent
D -->|Side Effect| J[GitHub PR Annotations]:::output
E -->|Side Effect| K[Automated Hotfix PRs]:::output
F -->|Side Effect| L[Terraform Drift Remediation]:::output
G -->|Side Effect| M[PagerDuty P1 Resolution]:::output
H -->|Side Effect| N[Auto-Generated Swagger/Docs]:::output
I -->|Side Effect| O[SBOM & CVE Patching]:::output
Each agent is strictly scoped and contextually provisioned with dedicated tools (API SDKs) and specialized meta-prompts.
- Heuristic Focus: SOLID principles, cyclomatic complexity, zero-trust security.
- Vector: Intercepts
pr_openedwebhooks, syntactically parses diffs, and injects actionable, context-aware PR comments directly into GitHub.
- Heuristic Focus: Deterministic build analysis, flaky test remediation.
- Vector: Ingests raw build logs upon Jenkins/GitHub Action failures, isolates stack traces, queries vector embeddings of internal docs, and occasionally submits an autonomous hotfix PR.
- Heuristic Focus: Multi-cloud cost anomaly detection, EC2 right-sizing.
- Vector: Interacts with AWS/GCP APIs, identifies over-provisioned clusters, and outputs declarative Terraform configurations to trim the fat without compromising high availability.
- Heuristic Focus: MTTR reduction, runbook execution.
- Vector: Parses raw PagerDuty/Datadog alerts, acknowledges incidents automatically, executes mitigation runbooks (e.g., rolling back a deployment or restarting a pod), and escalates to human operators only when absolutely necessary.
- Heuristic Focus: SAST/DAST, dependency graphing.
- Vector: Scans repositories for exposed secrets, evaluates OWASP Top 10 vulnerabilities, and forces compliance checks gating deployment.
Our stack is meticulously selected for high throughput, strict type safety, and maximum observability.
| Subsystem Layer | Technology Choice | Rationale |
|---|---|---|
| Cognitive Engine | GPT-4o / Claude 3.5 Sonnet | State-of-the-art reasoning and long-context window processing. |
| Agent Orchestration | LangGraph & CrewAI | Cyclical graph execution and role-based agent delegation. |
| Ingestion Gateway | FastAPI & Pydantic | Asynchronous, typed, high-performance webhooks (10k+ requests/sec). |
| Persistence & Telemetry | PostgreSQL & SQLAlchemy | ACID-compliant audit trails for every single LLM call and agent decision. |
| Containerization | Docker & Compose | Highly reproducible runtime environments parity across local, staging, and production. |
- 🐳 Docker Desktop
- 🐍 Python 3.10+ (for local bare-metal execution)
- 🔑 Valid API Keys (OpenAI / Anthropic)
We adhere to a declarative configuration approach.
# 1. Clone the repository
git clone https://github.com/IsmailSajid/AutoOps.git
cd AutoOps
# 2. Provision Environment Variables
cp .env.example .env
# > Edit .env with your LLM API Keys, GitHub Tokens, and AWS IAM Credentials
# 3. Ignite the Services (Dockerized via Compose)
docker-compose up -d --build
# 4. Tail the Cognitive Logs
docker-compose logs -fpython -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --host 0.0.0.0 --port 8000 --reloadNavigate to http://localhost:8000/docs to interface with the FastAPI interactive OpenAPI schema.
To simulate a real-world P2 incident or PR, bypass the third-party integrations and utilize our synthetic webhook endpoint:
curl -X POST http://localhost:8000/webhook \
-H "Content-Type: application/json" \
-d '{
"trigger_type": "pr_opened",
"repo": "IsmailSajid/Microservices-Core",
"branch": "feat/payment-gateway",
"payload": {"pr_number": 1024}
}'Monitor the telemetry logs to observe the CodeReviewAgent autonomously pulling the diff, analyzing cyclomatic complexity, and outputting feedback.
In true enterprise fashion, this system incorporates Zero-Trust architecture internally:
- Ephemeral Execution: Agents operate in bounded execution windows to prevent context bleed.
- Immutable Audit Trails: Every prompt, token count, and API call made by an agent is flushed asynchronously to a persistent PostgreSQL ledger.
- Human-In-The-Loop (HITL): All destructive actions (e.g., dropping production DB tables, hard-deleting cloud resources) strictly return a proposal to a human Slack channel rather than executing blindly.
- eBPF Kernel Tracing Agents: Agents that autonomously analyze Linux kernel packet drops using eBPF logic.
- Native Slack/Teams Middleware: A conversational interface for interacting with the swarm dynamically.
- Cross-Cluster Kubernetes Orchestration: Automated helm chart generation based on plain English architectures.
- Predictive Capacity Planning: Training custom ML models based on temporal workload data.
We are building the future of DevOps. If you're passionate about GenAI, MLOps, or large-scale distributed systems, fork this repository and submit a PR.
- Create a semantic branch:
git checkout -b feature/agent-parallelization - Commit your modular changes:
git commit -m "feat: migrate agent graph from sequential to parallel execution map" - Push to upstream origins.
- Let the
CodeReviewAgentevaluate your PR. 😉
Disclaimer: This software leverages advanced autonomy. Please rigorously test within isolated staging subnets before attaching IAM roles with AdministratorAccess.