An Agentic AI DevOps Bot that lives in Slack/MS Teams and automates Day-2 operations: incident triage, runbook execution, Kubernetes diagnosis, release helpers, PR reviews, on-call assistance, and cost signals.
Addressing the critical challenges in modern DevOps operations
High MTTD/MTTR due to scattered signals (logs/metrics/traces/chat/Jira/PagerDuty)
Repeated manual runbook steps during incidents and deployments
Context switching across Grafana, Kibana, Kubernetes, GitHub, Jira, and cloud consoles
Postmortems and release notes are manual and error-prone
Reduce MTTR by 25–50% with guided triage and automated runbooks
Cut toil (manual checks & repetitive commands) by 30–60%
Improve release quality (auto risk flags, PR summaries, conventional commit checks)
Better awareness (push cost/health digests; personalized on-call briefs)
From minimum viable product to advanced AI-powered operations
/bot triage payment-api latency spike → Bot correlates alerts (Prometheus), recent deploys (GitHub), error logs (Loki/CloudWatch), and suggests top suspects.
/bot k8s check ns=prod app=fx-api → Checks pods, restarts, OOMKill, readiness, image tags, HPA, recent events; proposes fixes.
/bot runbook scale fx-api to=6 → Guardrailed action that patches HPA/Deployment; records audit and posts diff.
Auto-review of PRs for risk hotspots, secret leaks, performance anti-patterns, and missing tests; summarizes for reviewers.
Generate release notes from commits, linked issues, and production incidents; post to Slack & Confluence.
At shift start: "Here are hot services, flaky alerts, SLO burn rates, and your 3 likely failure modes tonight."
Daily: "EKS nodepool X underutilized; suggest right-size to m6i.large. Projected monthly savings: £1,240."
Drafts timeline from PagerDuty, alert logs, Slack threads; fills a template for review.
Modalities: Chat (Slack/Teams), Web dashboard (Next.js) for history & approvals
FastAPI/Node: Slack/Teams slash commands, events, auth
LLM planning + tools (LangChain/CrewAI/AutoGen/TaskWeaver)
Secure adapters for Observability, Runtime, SDLC, Cost
RBAC + approvals, OPA/Rego policies for allowed actions
Vector store of runbooks, service docs, past incidents
All actions/events to Audit DB; metrics to Prometheus
Slack command → Bot Gateway → Agent with user/context
Agent plans → selects tools (Prometheus, K8s, GitHub) → gathers evidence
Agent composes diagnosis → if action required, requests approval
On approval → executes tool → posts result + stores audit
Slack user → corporate identity → SSO (OIDC) → JWT
Separate service accounts for read vs write tools
Only SRE group can scale prod, approval requirements
Vault/Secrets Manager; never pass raw creds to LLM
PII/secret scrubbing in logs and LLM prompts
Every tool call: who, what, when, why, inputs, outputs
Visual representation of the end-to-end solution showing all integrations and data flow
Chat Commands
Bot Integration
Dashboard UI
Request routing & rate limiting
Policy-based authorization
GPT-4 powered reasoning & tool orchestration
Metrics
Dashboards
Logs
AWS Monitoring
Orchestration
Containers
Package Manager
Source Control
Issue Tracking
Incident Mgmt
Spend Analysis
Cost Attribution
Optimization
Audit trails & configurations
RAG knowledge & embeddings
Caching & session management
Managed Kubernetes platform
Application deployment
GitOps continuous delivery
Comprehensive security framework built on OPA (Open Policy Agent) with role-based access control
Users can only access resources within their scope and permission level
All actions are denied by default unless explicitly permitted by policy
Complete audit trail of all actions, decisions, and access attempts
High-risk operations require explicit approval from authorized personnel
Temporary elevated permissions with automatic expiration
package devops_bot.authz
# Allow read-only queries by default
allow if {
input.action == "read"
input.user.authenticated
}
# Require approval for write operations
allow if {
input.action == "write"
input.approval_id
approval := data.approvals[input.approval_id]
approval.status == "approved"
approval.target == input.target
}
# Admins can perform emergency actions
allow if {
input.user.role == "admin"
input.emergency == true
log_emergency_access
}Declarative policy rules that govern access control decisions in real-time
| Action | Developer | SRE | Team Lead | Admin |
|---|---|---|---|---|
| View metrics/logs | ✓ | ✓ | ✓ | ✓ |
| Describe K8s resources | ✓ | ✓ | ✓ | ✓ |
| Execute pod commands | ⚠️ | ✓ | ✓ | ✓ |
| Scale deployments | ❌ | ⚠️ | ✓ | ✓ |
| Update configurations | ❌ | ⚠️ | ⚠️ | ✓ |
| Emergency actions | ❌ | ❌ | ❌ | ✓ |
Comprehensive tooling layer that provides secure adapters for all operational systems
Prometheus HTTP API; returns timeseries & summary
Kubernetes resource inspection and analysis
Execute commands in Kubernetes pods
Guarded Kubernetes resource modifications
Log aggregation and search capabilities
AWS CloudWatch log analysis
Recent deployment history tracking
Pull request analysis and summary
Git commit analysis and release notes
Incident management integration
Issue tracking and project management
Cost monitoring and anomaly detection
Resource optimization recommendations
Metrics, logs, and traces analysis
Kubernetes and infrastructure operations
Software development lifecycle tools
Financial operations and optimization
Important: Each tool returns structured JSON; the agent MUST explain reasoning before risky writes.
Core database schema for audit trails, approvals, and knowledge base management
Complete audit trail of all bot actions
Approval workflow management
Knowledge base and runbook storage
Pre-built prompt templates for consistent and effective AI agent interactions
You are a pragmatic SRE agent. Given metrics, logs, deploys, and traces, produce a short root-cause hypothesis with confidence, top 3 suspects, and the safest next step. Prefer read-only diagnostics unless explicit approval is present.
Core system prompt that defines the agent's role, behavior, and safety constraints for incident triage scenarios.
Template for incident analysis and diagnosis
Triage service: {service}. Context: {alerts} {deploys}
{logs_recent} {slo}. Output JSON with summary,
suspects[], next_steps[], missing_info[].Template for safe infrastructure changes
Prepare a minimal patch for {kind}/{name} to achieve:
{goal}. Validate namespace/policy and produce a
dry-run diff.Template for automated pull request analysis
Summarize PR {pr_number} in repo {repo}. Identify
security/perf risks, breaking changes, and tests
coverage gaps. Output summary, risks[], suggestions[].Runnable skeleton code that you can flesh out. Replace provider keys and wire real clients.
# gateway/main.py
from fastapi import FastAPI, Request
import httpx, os
from pydantic import BaseModel
app = FastAPI()
class SlackCommand(BaseModel):
user_id: str
text: str
channel_id: str
AGENT_URL = os.getenv("AGENT_URL", "http://agent:8001/run")
@app.post("/slack/command")
async def handle_cmd(cmd: SlackCommand):
async with httpx.AsyncClient(timeout=60) as client:
r = await client.post(AGENT_URL, json=cmd.dict())
return {"response_type": "in_channel", "text": r.json().get("message")}# agent/main.py
from fastapi import FastAPI
from pydantic import BaseModel
from typing import Dict
from tools import prom, k8s, git
from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, Tool, AgentType
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
tools = [
Tool(name="PrometheusQuery", func=prom.query,
description="Run PromQL. Input: promql string."),
Tool(name="K8sDescribe", func=k8s.describe,
description="Describe K8s app. Input: ns app."),
Tool(name="GitDeploys", func=git.recent_deploys,
description="Recent deploys for a service.")
]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
app = FastAPI()
class RunPayload(BaseModel):
user_id: str
channel_id: str
text: str
@app.post("/run")
async def run(payload: RunPayload) -> Dict:
q = payload.text
answer = agent.run(q)
return {"message": answer}Prometheus integration
# tools/prom.py
import os, httpx
PROM_URL = os.getenv("PROM_URL")
async def _http_get(url):
async with httpx.AsyncClient(timeout=20) as client:
r = await client.get(url)
r.raise_for_status()
return r.json()
def query(q: str) -> str:
"""Synchronous wrapper for agent demo; replace with async & parse results."""
import requests
r = requests.get(f"{PROM_URL}/api/v1/query", params={"query": q}, timeout=15)
r.raise_for_status()
data = r.json()["data"]["result"]
return str(data[:5]) # sampleKubernetes integration
# tools/k8s.py
from kubernetes import client, config
config.load_incluster_config()
v1 = client.CoreV1Api()
apps = client.AppsV1Api()
def describe(inp: str) -> str:
# input format: "ns=prod app=fx-api"
kv = dict(x.split("=") for x in inp.split())
ns, app = kv.get("ns", "default"), kv.get("app")
pods = v1.list_namespaced_pod(ns, label_selector=f"app={app}")
statuses = [{"name": p.metadata.name, "phase": p.status.phase} for p in pods.items]
return str(statuses)Git/GitHub integration
# tools/git.py
import requests, os
GH_TOKEN = os.getenv("GH_TOKEN")
def recent_deploys(service: str) -> str:
# placeholder: read releases/commits with label service
headers = {"Authorization": f"Bearer {GH_TOKEN}", "X-GitHub-Api-Version": "2022-11-28"}
r = requests.get("https://api.github.com/repos/acme/payments/commits", headers=headers, timeout=15)
return str([c["commit"]["message"] for c in r.json()[:5]])env:
PROM_URL: http://prometheus-server
GH_TOKEN: "<github-app-token>"
OPENAI_API_KEY: "<key>"
rbac:
rules:
- apiGroups: [""]
resources: ["pods", "events"]
verbs: ["get", "list"]
- apiGroups: ["apps"]
resources: ["deployments"]
verbs: ["get", "list"]A structured approach to building your AI DevOps Bot from concept to production
Repo layout (mono or multi): gateway/, agent/, tools/, web/, infra/
Slack app (slash commands + bot token), FastAPI gateway, health checks
Choose LLM provider + LangChain; set up prompt templates and memory
Implement prom_query, k8s_describe, loki_search
Slash: /bot triage <service> → correlates alerts + logs + recent deploys
Add approval workflow (Slack buttons) + OPA server
Implement k8s_patch for scaling with dry-run + diff preview
GitHub App with repo scope; summarize diffs, detect secrets
Flag risky files (infra, auth, payment paths)
Postgres for audit + pgvector for runbooks/KB
Ingest Confluence/MD runbooks
Dockerize, Helm chart, K8s deploy (staging)
GitHub Actions CI/CD, basic SLOs
Real-world scenarios showing how the AI DevOps Bot handles common operations
/bot triage service=fx-api symptom="P95 latency > 800ms"PromQL for histogram_quantile(0.95, rate(http_request_duration_seconds_bucket{service="fx-api"}[5m]))
Fetch recent deploys for fx-api (last 2h)
Logs: top 3 error signatures
Output summary + suspects + next steps
Ask for approval if action suggested (e.g., rollback)
/bot k8s check ns=prod app=fx-apiLists pods, restarts, last events
Image hash skew, HPA status
Pending pods reasons
Resource utilization analysis
Automated health recommendations
/bot runbook scale ns=prod app=fx-api replicas=6Produces diff; waits for /approve from SRE/lead
Applies patch; posts result and audit link
Updates monitoring dashboards
Notifies relevant teams
Records change in audit trail
On PR open: automated analysisSummarize changes, highlight risky files
Detect secrets via regex + entropy
Suggest tests; comment on PR
Performance impact analysis
Security vulnerability scanning
Comprehensive monitoring and performance targets for your AI DevOps Bot
Response time for bot commands
Reliability of tool executions
Track vs baseline improvement
Tool latency, LLM tokens, approvals
Master cutting-edge AI and automation concepts through hands-on implementation
Choose tools, interpret results, iterate intelligently through complex workflows
Implement safe automation with approvals and comprehensive policy frameworks
Leverage runbooks and service knowledge through advanced RAG implementations
Design sophisticated agent coordination (triager → fixer → reviewer) patterns
Transform your DevOps operations with intelligent automation. Get expert guidance on implementing this comprehensive AI DevOps Bot blueprint for your organization.