𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗧𝗵𝗮𝘁 𝗗𝗼𝗻'𝘁 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗲
Most AI agents fail in production. They invent function names. They guess wrong parameters. They call the wrong tools.
Function calling is supposed to stop this. In reality, it often makes agents confidently wrong at scale.
You do not need bigger models to fix this. You need better architecture.
Follow these four patterns to build reliable agents:
Use a two-stage router Do not pass 15 tools to a model at once. This causes schema bloat. Instead, use a fast, cheap model to classify intent first. Once you know the intent, only show the relevant tools. This reduces errors by 60-70%.
Enforce structured outputs Stop asking models to return valid JSON through prompts. Use schema enforcement at the API level. A schema wins over a 500-word prompt every time. It guarantees the model follows your rules.
Build validation layers Every tool call needs three steps:
- Pre-validation
- Model execution
- Post-validation
If a tool call fails validation, send the error back to the model. Models fix their own parameter errors 80% of the time on the second attempt.
- Set hard limits Infinite loops kill your budget. A model might call tools in a loop until it hits a timeout. Always set a maximum iteration count. Hard limits are a requirement, not an option.
Optimize your costs by using different models for different tasks:
- Use small models for intent routing and output formatting.
- Use mid-tier models for tool selection.
- Use frontier models only for complex planning.
This approach cuts costs by 10x to 15x with almost no loss in quality.
Reliable AI is about system design. Focus on constraints, validation, and orchestration.
What patterns do you use to build reliable agents?