What I Learned Running AI Agents in Production

I build AI systems. I talk to engineers who ship code. There is a gap between flashy demos and real production systems.

People call everything an agent now. A script with a loop is an agent. A chatbot with memory is an agent. This mistake leads to bad engineering.

Teams over-engineer simple tasks. They add complex orchestration to workflows that only need one good prompt.

An agent must have an objective, not just an instruction. It must decide what to do next. It must handle failure. It must know when it is finished.

Everything else is just a function call.

• 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 subtasks, it is a real agent.

Real agent deployments are narrow. They do one thing well like document extraction or code review. They are not general reasoning engines.

Successful teams focus on three things:

  • Tool design: Clean interfaces for what the agent calls.
  • Failure handling: What happens when a tool returns nothing.
  • Observability: Tracing why an agent made a specific decision.

Frameworks like LangChain or CrewAI change every month. The framework matters less than the patterns.

Use these patterns to succeed:

  • Plan then execute: Use one step for planning and a separate step for execution.
  • Separate retrieval from reasoning: Fetching context and using context are different jobs.
  • Explicit handoffs: Use structured logs when one agent passes work to another.

RAG is standard, but most people fail at chunking. If you split text poorly, the model loses context. If your RAG results are useless, check your metadata and chunking strategy before you blame the model.

Models will get better and cheaper. This does not change the core engineering challenge. You must build systems that behave correctly when you are not watching.

Focus on governance and observability. The engineers who matter will be those who build systems others can trust. This is systems design, not model research.

Source: https://dev.to/aibughunter/what-i-learned-after-running-ai-agents-in-production-for-a-year-49n