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How a managed AI DevOps team replaces platform-engineering labor: capability tiers, approval gates, audit trails, and the economics of autonomous infrastructure.
Both hyperscaler copilots are GA. Here they are on the five axes that decide an operations buy: scope and multi-cloud reach, billing shape, governance, audit trail, and BYOK posture.
Lightrun surveyed 200 SRE and DevOps leaders across US, UK, and EU. 43% of AI-generated code still requires manual debugging in production. 97% say their AI SREs operate without meaningful runtime visibility. AI assist isn't the operations layer.
Claude Mythos is 90x better at exploit development than Opus 4.6 and solves 73% of expert CTFs. Anthropic shipped it through Project Glasswing — 50 vetted defenders, $100M credit pool — and they surfaced 10,000+ zero-days in 30 days.
Google I/O shipped Antigravity 2.0, Managed Agents API, and ADK 2.0 — six weeks after AWS DevOps Agent GA. Two hyperscalers have now publicly shipped agentic-DevOps stacks. The category has crossed from research to GA-track.
AWS DevOps Agent GA shipped the first public hyperscaler-attributed labor-replacement numbers: 75% lower MTTR, 80% faster investigations, 94% root-cause accuracy. WGU collapsed an incident from 2h to 28 min.
Microsoft Security disclosed MDASH — a research harness that orchestrates 100+ specialized AI agents — and credited it with finding 16 new Windows CVEs, including 4 Critical RCEs in the kernel TCP/IP stack.
PraisonAI shipped its Flask API server with AUTH_ENABLED = False hardcoded. Anyone could trigger agent workflows without a token. Sysdig saw the first targeted exploit attempt 3h 44m after disclosure.
Self-hosted inference isn't a single-engine decision. SGLang and LMDeploy benchmark at ~16,200 tok/s on H100; vLLM lands at ~12,500. That 29% gap is $15K/mo at 1M req/day.
Most "AI-powered" DevOps tools wrap an LLM API and charge a 30-50% markup. BYOK flips that. The margins, procurement, and control benefits are structural — not a 20% discount.
Security scanners find problems. AI agents fix them. From detection to merged PR, autonomous remediation is dropping mean time to fix from weeks to hours.
Most SRE teams aren't Google. They're six engineers keeping twenty services alive. Here's the 2026 playbook — which agents reduce toil, what's safe to automate, what still needs a human.
Datadog, Honeycomb, Grafana, and Sentry all shipped MCP servers in Q1. Every coding agent is now a first-class SRE. Wire telemetry into Cursor and Claude Code without leaving the IDE.
Lightrun's 2026 report found 43% of AI-generated code still needs manual debugging in production. The problem isn't the model. It's the missing verification loop around the agent.
10 sec
to answer
Traditional dashboards show you data. An AI command center lets you ask questions, get answers, and take action — in one place.
24/7
autonomous
Agentic DevOps isn't a buzzword. It's the shift from dashboards to autonomous systems that audit, fix, and deploy. Here's what that looks like.
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steps
MCP lets AI coding tools talk to your infrastructure. Here's how to wire it into Cursor and Claude Code without standing up a new dashboard.
30 minutes with the team. We'll look at your cloud together, show you what we'd take off your plate, and scope a first month if it's a fit.