AI in SDLC: Cursor isn't enough
From a point assistant to platform context and measurable use cases
Slide contents
1. AI in SDLC: Cursor isn't enough
From a point assistant to platform context and measurable use cases
2. Alexander Polomodov
Technical Director & Fellow, large fintech
Fintech architecture and engineering practices
AI in SDLC at scale
Code of Leadership · @book_cube
3. From assistant to platform
Why point tools fall short
How big tech embeds AI
What already works inside Spirit
Where build-or-buy splits
4. 01. A tool ≠ a system
Local acceleration does not redesign enterprise delivery
5. Assistants change coding, not the system
Autocomplete became a baseline expectation
Vibe coding accelerates prototypes
Production still needs engineering constraints
Company context remains outside
6. Protocols expose context
MCP
Model Context Protocol
Access to context
Data and tools
A2A
Agent2Agent
Agent communication protocol
Task coordination
7. Platforms turn tools into developer jobs
8. Goals come before AI
Goal — What the engineer is completing
Hindrance — Where flow and feedback break
AI use case — Which action can be delegated
9. 02. Five cases — one pattern
AI amplifies a formal process and accumulated context
10. Formal rules make AI useful
11. The reviewer needs a second model
12. An LLM alone was not enough
13. Metrics mature with adoption
14. On-call agents need mature context
15. 03. AI inside Spirit
Company context, professional use cases, and quality evaluation
16. AI embeds into the platform
17. AI is already a platform bet
Respondent shares in State of Platform Engineering in the Age of AI
45% — Core strategy — Central component
34% — Important — Important, not central
83% — Already using — GenAI in dev stack
18. 04. Scale changes the answer
Buy, build, or connect public models to company context
19. Company size changes the answer
20. Cursor is only the first step
Rules and context precede models
Platforms connect AI to real work
Use-case metrics test the effect
Build-or-buy depends on scale
AI amplifies the engineering system already in place
21. What to read after the talk
Google — Measuring Developer Goals
Google — API Governance at Scale
ByteDance — BitsAI-CR
Red Hat — Platform Engineering and AI
22. Thank you!
polomodov.tech
All slides and links are in the Telegram channel
Alexander Polomodov, Technical Director & Fellow, large fintech
@book_cube