Executive Decision Agent
Hands-on in Yandex AI Studio: from knowledge base and search to calculations, options and an executive brief
Slide contents
1. Executive Decision Agent
Hands-on in Yandex AI Studio: from knowledge base and search to calculations, options and an executive brief
2. Alexander Polomodov
Technical Director & Fellow, T-Technologies
Architecture, engineering R&D, AI adoption.
Closes HSE series with agent loop.
Focus: agent for executive decisions.
3. Bring course topics into one loop
4. 01. What we build
Executive Decision Agent: an agent that prepares a board-level management brief
5. Not a chatbot: decision support
6. Question for the agent
Realistic request, safe data
Launch AI support next quarter?
Brief: options, economics, risks, checks.
Internal docs are primary facts.
Missing data → assumptions/questions.
7. Final answer: short and executive-ready
Decision, trade-offs, next steps
Brief structure
Decision in one paragraph.
3 options: launch/pilot/postpone.
Economics, risks, dependencies.
Management standard
Separate facts, estimates, recommendations.
Show sources and unknowns.
End with checks before decision.
8. 02. Yandex AI Studio
Which platform capabilities we use in the lab and why the agent needs them
9. AI Studio provides the agent prototype loop
10. Govern quality, operations and cost
11. Knowledge base grounds the agent
12. Web search is only market context
13. Calculation must be a tool
14. 03. Agent assembly
From system instructions and knowledge base to a calculation tool and first run
15. System instruction defines behavior
Clear rules beat long prompt
Prepare a CEO/COO/CIO executive brief.
Facts from knowledge base first.
Separate facts, assumptions, risks and options.
Missing data → owner questions.
16. Participants get a small simulated company
17. Minimal calculation schema
18. Launch prompt checks loop
Documents + search + calculation
Launch AI support assistant next quarter?
Prepare a one-page board brief.
Compare launch, pilot and postpone.
Show economics, risks and checks.
19. 04. Quality checks
The agent is ready only after scenarios where it fails safely
20. Test stable behavior
21. How to evaluate the result
We judge decision usefulness, not text polish
Facts: internal docs, no source substitution.
Calculation: reproducible economics.
Risk: guardrails, stop conditions, approval.
Format: concise and executive-readable.
Good agents aid decisions; bad agents write confidently
22. Human approval needed
23. 05. Decision review
Final debrief: where the agent helps and where it must be constrained
24. Lab plan
25. What each team should have
Working agent + path to org rollout
One-page brief for AI support launch.
Knowledge base + context + calculation tool.
Facts ≠ estimates; risks are visible.
Platform backlog: knowledge, tools, evals, access, cost.
Bridge from learning to AI-platform work
26. Links and materials
Docs and lab pack
Yandex AI Studio agents/search/function docs.
Lab pack + guide on GitHub.
Previous lectures in the series.