๐ง๐ต๐ฒ ๐ง๐ฟ๐ฎ๐ป๐๐ฝ๐ฎ๐ฟ๐ฒ๐ป๐ฐ๐ ๐๐๐๐จ๐ฒ ๐๐ป ๐๐ ๐๐ฒ๐ฝ๐น๐ผ๐๐บ๐ฒ๐ป๐๐ I spend a lot of time in the AI space. I read papers, build things, and talk to engineers. There is a gap between what demos show and what production systems look like. Nobody is being fully honest about it.
Everyone calls everything an "agent" now. But this dilution causes real engineering mistakes. When you do not have a precise definition, you over-engineer simple pipelines and under-engineer complex ones.
Here is what I think: an agent is a system with an objective, not just an instruction. It decides what to do next, handles failure, and knows when it is done.
- If your system needs a human to tell it each step, it is not an agent.
- If your system can recover from a failed tool call, you are getting somewhere.
- If your system can decompose a goal into subtasks, that is the real thing.
Most real agent deployments are narrow. They do one thing well. The teams getting good results are not chasing the latest model release. They are obsessing over:
- Tool design
- Failure handling
- Observability
The teams getting bad results are the ones that swapped out models without changing anything else. Source: https://dev.to/aibughunter/what-happens-when-you-run-10-ai-agents-at-once-in-a-real-codebase-j0 Optional learning community: https://t.me/GyaanSetuAi