๐ช๐ต๐ฎ๐ ๐๐ฎ๐ฝ๐ฝ๐ฒ๐ป๐ ๐ช๐ต๐ฒ๐ป ๐ฌ๐ผ๐ ๐ฅ๐๐ป ๐ญ๐ฌ ๐๐ ๐๐ด๐ฒ๐ป๐๐ ๐๐ ๐ข๐ป๐ฐ๐ฒ
Demos show one thing. Production systems show another. There is a massive gap between what people show in videos and how code actually runs in a real environment.
People call everything an agent right now. A chatbot with memory is an agent. A script with a loop is an agent. This is wrong. It leads to bad engineering.
An agent must have an objective. It does not just follow instructions. It decides what to do next. It handles failures. It knows when the job is done.
How to tell the difference:
- If a human must guide every step, it is a chat interface.
- If a system recovers from a failed tool call, it is an agent.
- If a system breaks a goal into tasks and delegates them, it is a real agent.
Most successful deployments are narrow. They do one job well. They handle support triage or document extraction. They are not general reasoning engines.
The best teams focus on three things:
- Tool design: How clean is the interface?
- Failure handling: What happens when a tool fails?
- Observability: Why did the agent make that decision?
The frameworks change every month. LangChain, CrewAI, or AutoGen do not matter as much as your patterns. Use these patterns instead:
- Plan then execute: Separate reasoning from action.
- Separate retrieval from reasoning: Do not mix fetching data with using data.
- Explicit handoffs: Use structured logs when passing work between agents.
RAG is standard, but most people do it wrong. The problem is often chunking. If your chunks break the context, the model will hallucinate. Fix your metadata and your chunking strategy.
Models will get better. Costs will drop. This does not change the main challenge. You must build systems you can trust when you are not watching.
The engineers who win will focus on systems design. They will focus on governance and reliability. They will build systems that other engineers can maintain.
Source: https://dev.to/aibughunter/what-happens-when-you-run-10-ai-agents-at-once-in-a-real-codebase-26ii