Author of the "Confident Idiot" post here (
https://news.ycombinator.com/item?id=46152838).
After that discussion, I looked at my own agent code and realized it was 80% error handling and 20% reasoning. I was manually decorating every function with retries, regex checks, and JSON validators. It was unreadable.
I realized that reliability shouldn't be Application Code; it should be Infrastructure.
I built Steer to test a pattern: Monkeypatching the framework to decouple reliability.
Instead of decorating functions, I initialize Steer at the entry point. It hooks into the framework's lifecycle (PydanticAI / OpenAI), introspects the tools, and attaches "Reality Locks" (SQL parsers, Schema checks, Entropy filters) globally.
Before (The Spaghetti):
# Business logic mixed with safety logic
@retry(stop=stop_after_attempt(3))
def run_query(q):
if "DROP" in q: raise Error() # Manual check
response = agent.run(q)
if not is_valid_sql(response): raise Error() # Manual check
return response
After (The Mesh):
import steer
# One line patches the framework globally.
# Auto-attaches SQL validators to any tool returning SQL.
steer.init(patch=["pydantic_ai"], policy="strict_sql")
# Pure Business Logic
agent.run(query)
It currently handles SQL AST validation, PII redaction, and a "Slop Filter" (using Shannon Entropy to block apologies).
It’s open source and local-first. I’m curious if anyone else is using this "sidecar" pattern or if you prefer explicit middleware?
Repo: https://github.com/imtt-dev/steer
