AI FinOps Agent
Controls model spend with routing, caching and per-workload budgets.
The problem. Inference cost is the line item nobody forecast: spend scales with adoption, the biggest consumers are invisible, and the reflex fix — a cheaper model everywhere — quietly damages quality.
The agent attributes every call to a workload, team and business outcome, so cost per interaction is a number the business can manage.
It routes each request to the cheapest model that passes the quality bar, applies prompt and response caching, and enforces per-workload budgets.
Quality is measured alongside cost, so a saving that degrades answers is caught rather than celebrated.
A single large bill with no attribution, and cost decisions made without quality evidence.
Cost per interaction is measured, budgeted and optimised with quality held constant.
Where this agent sits in the stack.
A modular stack that separates experience, orchestration, models and data — so use cases ship independently without a rebuild each time.
- 01
Experience & copilots
Web, mobile, CRM, service desk and Microsoft 365 surfaces where employees and customers meet AI — embedded in the tools they already use.
- 02
Orchestration & tooling
Prompt and agent orchestration, tool calling, routing between models, retries, fallbacks and cost/latency budgets enforced per workload.
- 03
Model layer
Azure OpenAI, AWS Bedrock, Vertex AI, Anthropic and open-weight models behind a common gateway with routing, caching and spend controls.
- 04
Knowledge & retrieval
Chunking, embeddings, vector and hybrid search, permission-aware retrieval and citation so every answer is traceable to an approved source.
- 05
Data & integration
Event streams, APIs, lakehouse and CDC pipelines connecting the stack to ERP, CRM, HCM and the core systems of record.
Integration surface
- Model gateway or provider APIs
- Cloud billing and cost data
- Agent runtimes for workload tagging
- Evaluation harness for quality checks
Guardrails & human oversight
- No routing change ships without passing the evaluation set.
- Per-workload budgets with alerting and hard stops on runaway spend.
- Every action outside policy stops at a reviewer queue with the agent's reasoning, evidence and proposed change attached.
- Cost and quality are always reported together, never separately.
What has to be true first
- Model traffic flowing through a gateway or otherwise instrumentable.
- An evaluation set to protect quality during routing changes.
- Agreed workload tagging so cost can be attributed.
Security, data & compliance
- Runs under a dedicated service identity with least-privilege, per-tool scopes — never a shared admin account.
- Customer and employee data stays inside your tenancy and region; no training on your data by default.
- PII is redacted before it reaches a model, and prompts, responses and tool calls are retained under your retention policy.
- Every tool call, input, decision and system write is logged and replayable for audit and model-risk review.
How this agent reaches production.
Weeks 1–2 · Scope
Traffic and spend baseline, workload tagging model, quality bar agreed.
Weeks 3–6 · Build
Gateway routing, caching, budgets, attribution dashboards and CI quality gate.
Weeks 7–10 · Production pilot
Top workloads optimised with before/after cost and quality reporting.
Quarter 2+ · Scale & run
All workloads governed, with monthly FinOps review and forecasting.
What we agree to be measured on.
Ranges drawn from comparable production engagements. Your baseline is agreed before build starts, and the same numbers are reported after go-live.
| Metric | Expected range |
|---|---|
| Cost per AI interaction | 40–70% lower |
| Spend attributed to a named workload | 95%+ |
| Quality change during optimisation | Held flat on the evaluation set |
| Forecast accuracy on monthly AI spend | Within 10% |
Model the business case: AI Run Cost calculator →
Top workloads instrumented and optimised with a before/after cost and quality report.
Fixed-price scope · milestone billing · price on request.
Enterprise AI FinOps practice with budgets, forecasting and monthly governance reporting.
Retained pod · quarterly outcome review · price on request.
The full AI FinOps Agent specification, as a PDF.
A multi-page specification your architecture, security and procurement reviewers can read without a call: what the agent does, the architecture, the integration surface, autonomy and guardrails, security posture, rollout plan, measurement plan and engagement shape.
- Process before and after, with the decision that stays with a human
- Layered architecture diagram and named integration surface
- Guardrails, approval gates, escalation and audit trail
- Security, data handling and compliance posture
- Phase-by-phase rollout and the measurement plan
The FinOps playbook for LLM + CCaaS spend
How to control LLM and CCaaS spend before it controls you. Token analytics, model routing, license rightsizing, telephony minute optimization and a board-level cost dashboard you can copy directly.
Read the playbook →- Audit-grade agent observability and tracing
100% of cases reconstructable step by step · drift detected before member impact · 30% shorter compliance approval cycle
- Agent governance program with evaluation gates and audit trail
100% of deployed agents pass a standard evaluation gate · 30–50% shorter path from pilot to production
- Control plane for a hybrid human and AI agent workforce
Single capacity view across human and AI agents · cost per contact reported by agent type within 1 quarter
Get a written estimate for the AI FinOps Agent.
Tell us the process, the systems it touches and the compliance scope. We come back with a scope, a measurement plan and a written estimate — no published band that would not apply to you.
solutionAI FinOps Agent — routed to this team
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