Langdock integration evaluation (Biorelate)
What it is
An integration evaluation: could Langdock sit alongside Biorelate’s own stack and extend what we offer to clients without compromising data handling, citation integrity, or evaluation discipline?
Langdock is an enterprise AI platform that lets organisations deploy LLM-powered workflows across teams under a single managed interface. The question for Biorelate Professional Services was operational: where does it fit in our delivery model, and where doesn’t it.
What I did
- Designed and ran the evaluation. Defined the criteria up front: data sovereignty, citation handling, evaluation harness, observability, deployment topology, and cost.
- Built test harnesses for the parts that matter to clients: agentic round-trips, tool use, output sanitisation, citation correctness.
- Surfaced where Langdock is strong (managed infra, fast iteration, multi-model access) and where it has rough edges (round-trip topology under multi-step agent runs, observability gaps in certain step-count regimes).
- Documented integration patterns so the wider Biorelate team can plug Langdock into client engagements without relearning the gotchas.
Outcome
- A validated integration path with clear “use this when, don’t use this when” guidance.
- Reusable test fixtures and a small library of integration patterns.
- Faster onboarding for any future engagement that wants to lean on Langdock.
What I learned
A managed-AI platform’s marketing materials rarely describe its actual round-trip topology. Test harnesses that exercise agentic behaviour end-to-end (not just single-prompt completions) reveal the operationally important quirks. In Langdock’s case the requestMaxSteps setting affects round-trip topology, not just step count, which is the kind of thing you only catch with proper instrumentation.