Sponsio: Deterministic Contract Layer for LLM Agents [P]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
We've been trying to put LangGraph agents into production for a while. The thing that kept biting us was tool-call boundary enforcement: stuff like "must call X before Y", "max N retries", "approval gate before destructive action". Worked fine in demos, broke at the moments that mattered.
What we tried first:
Prompt engineering. Told the model "always call check_policy before issue_refund". Worked ~95% of the time. The 5% that didn't was exactly the cases an auditor would ask about. Not a great answer when someone wants to know why a refund went through.
Post-hoc audit (OTEL + log). Caught violations after the fact. By then the side effect already happened. Refunding the refund is awkward.
Pulling everything into a workflow engine (Temporal, or nano-vm more recently). Strong guarantees but you rewrite the agent against their runtime. Too much for our use case.
What we ended up with:
A contract layer at the tool boundary. YAML rules, deterministic eval, runs before the tool call commits. Open-sourced as Sponsio.
Repo: github.com/SponsioLabs/Sponsio
Would love feedback from anyone running agents in prod.
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