FinOps Anomaly Copilot
An AI agent that explains Azure cost anomalies in plain language and recommends concrete next steps, extending the FinOps advisory work in the Sales Engineering case study.
AI-generated / synthetic demo data
Every explanation on this page is generated live by an AI agent (Microsoft Foundry, prompt-based) built for this demo. The cost-anomaly scenarios are synthetic, illustrative examples modeled on common real-world FinOps patterns — not a real Azure subscription, invoice, or customer data. This is a portfolio product demo, not a monitored production FinOps tool.
How it works
FinOps Anomaly Copilot is a Microsoft Foundry prompt agent wired into the same Azure Function that powers this site's portfolio chatbot. Pick one of the six bundled demo scenarios below — each modeled on a common, well-documented Azure cost-anomaly pattern — and the agent reads a structured description of that anomaly and writes a plain-language explanation with concrete recommended actions, live, in the browser.
The agent only ever sees the fixed scenario record you select. It cannot access a real Azure subscription, and visitor input is limited to choosing a scenario id, so there is no path for arbitrary text to reach the model as an unbounded prompt.
Try it
Select a demo cost anomaly, then ask the copilot to explain it.
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Pick a scenario above to begin.
Why this exists
This product extends the FinOps advisory pattern from the Sales Engineering project record: translating cost and spend signals into decisions a non-specialist stakeholder can act on. The same explanation structure a human FinOps advisor would use in a cost review — what happened, why, what to do, and what to watch — is encoded directly into the agent's instructions.
Production checklist
- Structured, allow-listed inputs only — no free-text cost data reaches the model.
- Dedicated Foundry prompt agent, versioned and independently rate-limited from the portfolio chatbot.
- Scheduled daily trace evaluation (task completion, coherence) on live usage.
- Explicit synthetic-data and AI-generation disclosure on every response.
- Automated backend tests cover validation, rate limiting, and failure handling.