Automate financial query management
A Copilot Studio agent answers common finance questions and logs them for quality control.
A custom agent answers employee finance questions from a knowledge base, gathers feedback, and escalates complex issues — reducing the team's query workload.
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 "Automate financial query management" 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.