Finance Q&A agent
A Copilot Studio agent resolves finance queries against ERP and policy data with compliance alignment.
The agent answers finance questions (invoice status, payments, expense tracking) from ERP and policy sources, routes complex queries to SMEs, and reports recurring gaps back to the team.
Retrieval-grounded assistant (rung 3 of 6). Fetch your evidence, then answer. Fixed path, read-only.
Not an agent: the sequence of steps is fixed in advance, so the whole path can be tested end to end.
- Autonomy: T0 · Suggest
- Surfaces information, ranks, or recommends. Changes nothing. Human role: does all the work; may ignore the system entirely.
- How it is governed
- Cite-or-abstain. Source freshness and access controls inherited from the corpus.
- Anthropic (Claude Certified Architect – Foundations (CCAR-F))The augmented LLM: a model with retrieval. source
- OpenAI (OpenAI agent guidance (Agents SDK, Codex, OpenAI Academy))Data tools that retrieve the context the answer needs. source
- Google Cloud (Generative AI Leader)Grounding in first-party enterprise data; retrieval-augmented generation (exam §3.3). source
- Microsoft (AI-901 (Azure AI Fundamentals) and AB-100 (Agentic AI Business Solutions Architect))Knowledge sources and grounding data for Copilot Studio agents (AB-100). source
How Koko classifies AI · the full ladder is taught in AI & Agentic Delivery Foundations.
Try it
Illustrative demoWorked example of "Finance Q&A agent" on synthetic data — edit the inputs to tailor it. Edit the inputs to tailor the output — it's baked synthetic data, so nothing leaves your browser until you run it live.
Synthetic demo company (a ~$5B high-growth US AI SaaS) — swap in any name to tailor the output.
What this run should concentrate on.