The Exact Stack I Use to Build Production AI Agents

Demos show one thing. Production systems show another. There is a gap between them that many people ignore.

People call everything an agent now. A chatbot with memory is an agent. A script with a loop is an agent. This mistake leads to bad engineering. You end up over-engineering simple tasks and under-engineering complex ones.

An agent must have an objective. It should not just follow an instruction. A real agent decides what to do next. It handles failures. It knows when the work is done.

Check your system against these rules:

  • If a human must guide every step, it is a chat interface, not an agent.
  • If the system recovers from a failed tool call, you are building an agent.
  • If the system breaks a goal into subtasks, you have a real agent.

Successful teams do not build general reasoning engines. They build narrow, purpose-built pipelines. They focus on three things:

  • Tool design: Making clean interfaces for the agent to call.
  • Failure handling: Deciding what to do 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 to create a plan and a separate step to follow it.
  • Separate retrieval from reasoning: Fetching context is a different job than using it.
  • 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 and hallucinates. If your results are useless, check your metadata and chunking strategies before you blame the model.

The challenge is not chasing benchmarks. The challenge is building systems you can trust to work when you are not watching. Focus on governance, observability, and reliable tool use.

The best engineers will focus on systems design, not just prompt engineering. Build systems that other people can maintain.

Source: https://dev.to/aibughunter/the-exact-stack-i-use-to-build-production-ai-agents-no-fluff-2lmp