๐ง๐ต๐ฒ ๐๐ฎ๐ฝ ๐๐ฒ๐๐๐ฒ๐ฒ๐ป ๐๐ ๐๐ด๐ฒ๐ป๐ ๐๐ฒ๐บ๐ผ๐ ๐๐ป๐ฑ ๐ฃ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป ๐ฅ๐ฒ๐ฎ๐น๐ถ๐๐
AI agent space moves fast. OpenAI and Google race for the next interface. Companies worry about lock-in and costs.
Most demos show one task. One session. Clean environment.
Real production is different. It needs persistent autonomy. It must run for days. It must handle errors without humans.
This is a different engineering problem.
Deployments fail when you ignore these:
- API failures at 2 AM. Does the agent retry or corrupt data?
- Context drift. Agents lose focus after six hours.
- Security. Agents with file access are risky without limits.
- Accountability. Who is responsible when an agent fails?
How successful teams win:
- Set guardrails. Use hard limits.
- Build monitoring. See every tool call and decision.
- Plan failure modes. Use runbooks.
- Use incremental autonomy. Start with human-in-the-loop. Move to full autonomy slowly.
This is the unsexy truth. Demos are easy. Scale is hard.
The next year separates teams who know production from teams who know demos. The technology is ready. Operational maturity is not.
What is your experience? Did your agent fail in production?
Source: https://dev.to/tarunai/the-gap-between-ai-agent-demos-and-production-reality-49nk