Service control plane
Map critical services, owners, dependencies, SLAs, SLOs and escalation paths into a single operating model.
// INFINITY SERVICE OPS
Service Operations Engineering for AI-era production systems.
Turn fragile operations into a resilient service control plane: SOE runbooks, SLOs, observability, incident command and AOE automation with MCP-ready guardrails.
SOE · Service Operations EngineeringAOE · AI Operations EngineeringMCP-ready automation
∞
SLOMCPPIRLMT24/7Ops-ready patternsincident, change, escalation
SLOReliability targetsburn-rate + ownership
MCPAI tool operationssafe actions, full audit
30dCutover roadmapfrom discovery to cadence
// FREE DEV TOOL
OpsForge Service Blueprint
A lightweight readiness canvas for MCP-era service operations. Use it to identify weak controls, unsafe automation candidates and the fastest path to calmer production support.
01
Inventory service ownership, integrations, data flows and failure modes.
02
Score current SOE maturity across incident, change, monitoring and knowledge.
03
Select AOE automation candidates with low blast radius and high operator value.
04
Ship a 30-day action plan with dashboards, runbooks, controls and review cadence.
// SOE
Service Operations Engineering
SOE creates the operating system for your services: who owns what, how reliability is measured, how incidents are handled and how knowledge becomes repeatable execution.
Incident command system
Define severity levels, commander roles, comms templates, stakeholder updates and post-incident review loops.
Runbook engineering
Convert tribal knowledge into tested runbooks with checks, rollback points, evidence capture and automation candidates.
Observability topology
Design logs, metrics, traces, dashboards and alerts that align to real user journeys instead of noisy infrastructure.
// AOE
AI Operations Engineering
AOE adds AI assistance without losing control. Agents can read, reason and recommend first; controlled execution comes only after permissions, evals, telemetry and rollback are proven.
MCP-aware ops automation
Expose approved operational actions as safe tools with clear permissions, audit trails and human-in-the-loop gates.
AI support copilot design
Build support assistants that retrieve runbooks, summarise incidents, draft updates and recommend next checks.
Agent evaluation harness
Test AI operations workflows against golden tickets, unsafe-action traps, latency budgets and recovery scenarios.
Automation adoption path
Move from read-only insight to guided actions, then controlled execution once evidence, approvals and rollback are mature.
// DELIVERY MODEL
From noisy support to reliable service cadence
Discover
Stabilise
Instrument
Automate
Review
// FAQ
Service Ops FAQ
Who is Infinity Service Ops for?+
Founders, platform teams, IT leaders and support organisations that need calmer production operations, clearer ownership, better incident response and practical AI automation.
Does this replace my current ITSM tooling?+
No. The first goal is to improve the operating model around your tools. Automation and MCP integrations can then connect Jira, ServiceNow, GitHub, Slack, Teams or custom systems safely.
What is delivered in an SOE engagement?+
A service map, SLO catalogue, incident model, runbook set, observability recommendations, escalation matrix and practical improvement backlog.
What is delivered in an AOE engagement?+
An AI operations automation design: tool permissions, prompt and retrieval architecture, eval cases, guardrails, telemetry, rollout stages and acceptance criteria.
How does this connect with MCP-related tools?+
Service Ops uses MCP patterns to make operational tools discoverable, permissioned and auditable, so agents can assist without bypassing service controls.
// NEXT STEP
Build the calm, observable, AI-assisted ops layer.
Start with the free blueprint, then turn the findings into an SOE/AOE roadmap your team can ship.