War Story of putting AI Agents into Production [ukr]

Nowadays, everyone is creating AI Assistants to help us in our day-to-day activities and "vibe coding" nice and shiny demo apps. However, when you try to bring AI Assistants to life in production—especially to be used by the citizens of a Gov Portal—you realize that creating the AI Agent itself is only 10% of the effort required. The other 90% is spent on satisfying cross-cutting concerns and different stakeholder concerns.

This talk will be based on my own experience of putting citizen AI Assistants for a Gov Portal (built on top of an AI Agentic Platform) into production. I'll talk about the challenges we faced, concerns we received from different groups of stakeholders, important NFRs and quality attributes of the solution, and possible solutions for these challenges. This talk will give useful tips, provide a wider view on real-world challenges, and improve critical thinking.

This talk will be interesting for diff Roles to get a wider view on real world challenges and to improve critical thinking.

Amongst other topics I want to talk about (not in order for now):
- Naive implementation of Chatbot -vs- scalable and mature implementation
- Benefits and drawbacks of using AI Agentic Platform -vs- custom implementation
- Choice of Public LLM vs Private LLM
- Performance and quality challenges
- Infrastructure
- Costs
- Error handling
- UI/UX
- Guardrails and protection against misuse and DDOS attacks
- Observability
- Multilingual support
- Testing and evaluation framework
- PII data handling
- and more...