SDM · Enterprise Agentic Framework · Publicis Sapient
Harness engineering · cost and performance optimization for LLM systems
I work on the Enterprise Agentic Framework team, building the harness layer that sits between application code and the models underneath it — provider abstraction, execution orchestration, caching, failure handling, and the accounting that tells you what any of it actually cost.
The model gets the attention. The harness decides whether the system is usable: whether a run finishes in fifteen minutes or four hours, whether a provider outage degrades or takes the whole job down, whether a repeated request costs full price or nothing at all.
Provider abstraction. One calling surface across gateways and vendors, with model aliasing, per-model capability differences, and graceful degradation when a provider rejects a parameter it doesn't support — the request should lose the hint, not fail.
Execution and concurrency. Turning serial pipelines into bounded parallel ones without losing determinism or result ordering. Most long-running inference jobs are latency-bound, not compute-bound, and spend the majority of their wall-clock time waiting.
Failure semantics. Timeouts on every network path, bounded retries with backoff, and — the one that bites hardest — never letting a failed call be recorded as a completed one. A resumable pipeline that caches its own errors will happily finish, fast and wrong.
Caching and resumption. Content-addressed reuse so interrupted work resumes instead of restarting, with cache keys that actually reflect every input that changes the output.