Reliability Engineering for LLM Applications
Building production guardrails that keep language model features predictable and debuggable.
Structured Outputs First
The fastest win was teaching our agents to return JSON instead of prose. We defined strict Pydantic schemas for every tool result and forced the models to use `response_format` templating. When the model drifted, the parser failed fast and we could trigger a deterministic retry with a clarified prompt.
We also versioned every schema and stored it alongside the prompt. When a breaking change landed, the router could downgrade a tenant to the previous schema without redeploying models.
Regression Testing with Synthetic Data
Manual evaluation does not scale, so we generated hundreds of synthetic conversations using real customer transcripts as seeds. Each test case asserted both structural correctness and domain-specific business rules (e.g., “never promise overnight shipping to Alaska”).
A dedicated GitHub Action replays the suite against every new prompt or model version. Failures block merges and produce a diff that highlights which assertions broke.
Operational Feedback Loops
We pushed all agent invocations through an OpenTelemetry pipeline so you can trace a bad answer back to the prompt, model, tool calls, and retrieved documents. Errors roll up into weekly reliability reports shared with stakeholders.
When a tenant reports an issue, we can reproduce the exact conversation locally thanks to deterministic seed values and stored retrieval snapshots. Mean time to resolution dropped from days to hours.
Key takeaways
- Enforcing JSON schemas eliminated broad classes of failure and gave us clean retry semantics.
- Automated synthetic regression suites catch prompt drift before it hits production.
- Full telemetry on every agent call shortens the feedback cycle with customers.
Need help implementing this?
I work with teams to turn these practices into production workflows.