AniSri
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AI engineering & AIOps

Put AI in production with evidence, boundaries, and an exit.

Assess coding agents and context, model gateways, RAG, evals, LLM and agent observability, secure tool use, cost controls, and bounded automation in production operations.

AI engineering owns model and agent behavior. AIOps applies those systems to operations. Reliability still owns production risk and approval boundaries.

8
Focus areas
22
Implementation patterns
Assessment structure
Questions, evidence, and implementation patterns

What this capability should change

01

Model, prompt, retrieval, tool, and policy changes pass task-specific evaluations before release.

02

Every AI request can be traced across context, model calls, retrieval, tools, cost, and final outcome.

03

Agents operate within enforced permissions, budgets, exit conditions, and human approval gates.

Focus areas

Select an area to open its questions, evidence, target state, and implementation patterns.

Why it matters

Establish a deliberate AI strategy that increases impact while preventing runaway costs, quality regressions, and over-reliance on AI-generated output.

Discovery Questions

  • •Does the organization have a written AI adoption strategy with clear use-case boundaries?
  • •How are AI tools evaluated before broad rollout (pilots, evals, metrics)?
  • •What guardrails prevent teams from shipping unreviewed AI-generated code or content?
  • •How is AI tool spend tracked, attributed, and optimized?
  • •Is there a responsible AI policy covering bias, hallucination, and data privacy?
  • •How are AI capability gaps identified and addressed through training?
  • •What feedback loops exist to measure whether AI tools are improving outcomes?

Evidence to Collect

  • •AI adoption policy or strategy document.
  • •Pilot results and eval scorecards.
  • •AI spend dashboards.
  • •Training or enablement materials.

What good looks like

A governed AI portfolio records approved use cases, data and action boundaries, evaluation results, accountable owners, spend, production outcomes, and review requirements before tools or models receive broader access.

Implementation Patterns

AI Center of Excellence (CoE)

Stand up a lightweight CoE to own AI strategy, evaluate tools, and share patterns across teams.

Internal WikiSlack/TeamsOKR Tooling
Steps
  1. Define CoE charter: scope, membership, decision rights, and cadence.
  2. Maintain an AI tools registry with adoption status, cost, and use cases.
  3. Publish and iterate on AI usage guidelines and acceptable-use policies.
  4. Run quarterly AI retrospectives: what worked, what was wasteful, what to cut.
  5. Track AI ROI metrics: developer velocity delta, incident MTTR, test coverage gains.

Responsible AI & Governance Framework

Encode ethics, safety, and accountability into every AI initiative from the start.

NIST AI RMFEU AI ActOWASP Top 10 for LLM ApplicationsInternal Policy
Steps
  1. Define data classification rules for what can be sent to external LLM APIs.
  2. Implement output review gates before AI-generated artifacts reach production.
  3. Require human approval for any agentic action with destructive side-effects.
  4. Log all AI API calls for auditability and cost attribution.
  5. Threat-model prompt injection, insecure tool use, data exfiltration, and excessive agency.
  6. Red-team model and agent workflows before launch and after material tool or permission changes.
  7. Run bias and hallucination audits on models used in decision-making workflows.

See it applied

Sentinel SRE

An AI incident investigator that gathers operational context and proposes findings and fixes for human review.

Platform playbook

Practical defaults, failure modes, and copyable checks

Browse the full playbook

Filter by layer, search the collection, and copy the useful part from the full playbook.