Case study
FDEAgentic AI
Pharma digital twins & synthetic persona evals
Built digital-twin and synthetic-persona evaluation flows with Langfuse-backed tracing for regulated pharma agent workflows.
Global pharmaceutical organization · ~6 min read
Anonymized case study. Metrics marked [TBD] pending client validation. Status: draft.
At a glance
Eval scenarios covered
[TBD: N]
Distinct digital-twin and persona scenarios in the regression suite
Regression pass rate
[TBD: %]
Automated eval suite pass rate on main agent branch
Time to release
[TBD: days]
Agent change promotion from dev to production-adjacent review
Reviewer cycle time
[TBD: hours]
Domain SME review turnaround on eval reports
Problem
Pharma stakeholders needed realistic scenario coverage for agentic workflows without exposing production patient data during early iteration. Manual spot checks and ad hoc prompts could not represent the breadth of clinical and commercial personas or the edge cases compliance reviewers expect to see before any production-adjacent deployment.
Ad-hoc manual testing failed to scale across persona variants, regression after model or prompt changes, and the traceability requirements of regulated review. Teams lacked a shared harness that tied synthetic scenarios to observable agent behavior, reviewer-friendly reporting, and repeatable gates before release.
Approach
- Scenario design with domain SMEs mapped target clinical and commercial workflows to digital-twin scenarios and synthetic persona datasets that stress realistic dialogue paths without live PHI.
- A Langfuse eval harness implemented traced eval runs, regression suites, and reviewer-facing reports so each agent change carried attributable pass/fail evidence and drill-down into failure modes.
- Production-adjacent deployment patterns aligned harness execution, secrets handling, and promotion workflows with the client's regulated environment constraints.
- Embedded FDE delivery ran discovery with domain experts, iterated on harness coverage weekly, and documented handoff so client engineering could own ongoing eval operations.
Results
[TBD: Insert validated metrics before publish.]
- Synthetic persona and digital-twin libraries gave product and compliance stakeholders a shared vocabulary for scenario coverage before live data access.
- Langfuse-backed traces linked each regression run to prompts, tool calls, and outcomes reviewers could audit without re-running manual sessions.
- Release gating moved from informal sign-off to eval-suite thresholds, reducing surprise failures in staging-like environments.