Service & IT Operations AI Playbook

Why this playbook exists

Most incident tooling accelerates visibility but not decisions. In real operations the questions are: Why was this categorised that way? Why this team? What's the evidence? This playbook answers those at the architecture level. We combine outcome-ranked retrieval (linking symptoms to similar incidents, changes, and KBs), governed orchestration (HITL, approvals, execution controls), and a learning memory that turns resolver feedback into signals-so triage accuracy, routing precision, and fix quality improve together.

What it delivers

A governed, evidence-backed assistant that shortens MTTR, improves first-contact resolution, and strengthens change assurance. Incident classifications and recommendations show their working with links to prior incidents, changes, and KB articles. SLA-aware routing learns from outcomes, balancing speed and quality. Resolver feedback and edits feed the learning loop, refining future triage and fix drafts. Change approvals, rollback points, and full audit for high-severity actions. Positioning: Not just another AI bot-this is a governed ops workflow that learns, built on The NeuralHue Framework.

How it works

An incident arrives from the ITSM platform or monitoring. The agent queries NeuralHue Knowledge Integration to retrieve similar incidents, recent changes, and relevant KB/runbooks-returning anchored citations to evidence. The Incident Triage service proposes category, priority, and likely impact with rationale. The SLA Router suggests the best resolver group, exposing confidence, alternatives, and expected time to respond based on history. Before any execution, Governance & Alignment enforces policy: change-related actions require approvals; role-based controls separate read-only diagnostics from write/execution steps; high-severity incidents capture additional evidence automatically. Resolver edits and outcomes (correct/incorrect category, reassignment, successful fix) are captured by the Memory & Feedback Engine, improving retrieval weighting and routing logic. Over time, the system spends less time guessing and more time reusing proven fixes-with provenance.

Core agents & workflows

Incident Triage
Proposes category, priority, and impact with links to similar incidents and relevant KB entries. Surfaces uncertainty explicitly.

SLA Router
Routes to the best resolver group based on historical outcomes, availability, and SLA risk; shows confidence and alternative options.

Root-Cause Assistant
Correlates incidents with recent changes, deploys, and signals from observability; proposes probable causes and next diagnostic steps.

Fix Drafting Copilot
Drafts runbook steps, rollback plan, user comms, and post-incident summaries-each item cited to evidence (KB, prior RCAs, change tickets).

(All agents inherit memory, governance, and orchestration policies from The NeuralHue Framework.)

Governance, risk, and assurance

Governance is embedded, not bolted on: Change approvals (CAB/expedited) for execution steps and high-impact fixes. Separation of read-only vs execution permissions; just-in-time elevation with logging. Rollback points and change windows enforced by policy. Immutable audit trails: what evidence was retrieved, who approved, what was executed, and when. Fairness/drift monitors on routing to prevent team overloading and detect quality degradation.

Data & integrations

Systems: ITSM (ServiceNow/JSM), CMDB, observability (logs/metrics/traces), CI/CD, runbook/KB repos (Confluence/Wikis/Git), status pages. Identity & security: SSO, SCIM, RBAC; zero-trust networking; data residency controls; VPC/on-prem supported. Models: OpenAI/Anthropic/Llama or local inference via Ollama-model choice is policy-controlled. Events: Ingest from alerting/monitoring; post decisions to chat (Slack/Teams) for approvals.

Outcomes & indicative KPIs

Triage-to-assignment time: 20-40% faster on targeted categories. First-contact resolution: ≥25% improvement for top 5 categories after 6-8 weeks. MTTR: Material reduction for repeating patterns via better correlation to changes. Change assurance: ≥95% of executed fixes carry rollback plans and approvals with citations. (Targets are indicative; final acceptance criteria are set during pilot planning.)

90-day pilot plan

Weeks 1-2 - Foundations
Read-only ITSM integration; import KB/runbooks and CMDB links; connect observability signals; baseline metrics.

Weeks 3-6 - Shadow mode
Enable Incident Triage and SLA Router in advisory; capture resolver feedback on category/assignment; publish governance dashboards.

Weeks 7-10 - Execution governance
Turn on Root-Cause Assistant correlations; enable Fix Drafting Copilot for top 5 categories; configure change approvals and rollback templates.

Weeks 11-13 - HITL & scale plan
Selective execution via approvals; KPI review; tuning of routing fairness and uncertainty thresholds; go/no-go and rollout plan.

Deliverables: anchored KB corpus, agent configs, routing policy, governance dashboards, approval/rollback templates, KPI report, rollout runbook.

Architecture (where this sits)

FAQs

Can it run without any execution rights?
Yes. Operate in advisory-only mode first (triage, routing suggestions, fix drafts) and enable execution later with approvals.

Will it overload the most effective team?
Routing fairness monitors track load and quality; policies can cap allocations and suggest viable alternates.

Does it replace our ITSM or observability stack?
No. It orchestrates and learns across them, adding explainability, governance, and evidence capture.