Most current approaches to agent reliability involve mixing validation logic (regex checks, JSON parsing, retries) directly with application logic (prompts/tools). This usually results in decorators on every function or heavy try/except blocks inside the agent loop.

I’ve been experimenting with an alternative architecture: an Agent Service Mesh.

Instead of decorating individual functions, this approach involves monkeypatching the agent framework (e.g., PydanticAI or OpenAI SDK) at the entry point. The “Mesh” uses introspection to detect which tools or output types the agent is using, and automatically attaches deterministic validators (what I call “Reality Locks”) to the lifecycle.

The Architecture Change:

Instead of tight coupling: python @validate_json # <--- Manual decoration required on every function def run_agent(query): ...

The Service Mesh approach (using sys.meta_path or framework hooks): “`python

Patches the framework globally.

Auto-detects usage of SQL tools or JSON schemas and attaches validators.

mesh.init(patch=[“pydantic_ai”], policy=”strict”)

Business logic remains pure

agent.run(query) “`

I implemented this pattern in a library called Steer. It currently handles SQL verification (AST parsing), PII redaction, and JSON schema enforcement by hooking into the framework’s tool-call events.

I am curious if others are using this “sidecar/mesh” approach for local agents, or if middleware (like LangSmith) is the preferred abstraction layer?

Reference Implementation: https://github.com/imtt-dev/steer