"It just gave a weird answer" is undebuggable
Without visibility into what was actually sent to the model, you're guessing whether it was a bad prompt, bad retrieval, or a bad guardrail decision.
SolutionsDebug why the
Ship With Confidence
Debug why the
model said that.
Every intent call produces a full execution trace automatically — prompt, retrieved context, guardrail checks, and final output, all in one queryable place.
Start with your domainKeep ownership and runtime context with the team that operates the capability.
Expose a clear contractIntegrate through a named intent instead of coupling product code to prompts.
Improve from evidenceTest and observe the same execution path your application calls.
Where teams get stuck
The prototype is easy. The operating model is not.
Production problems appear when prompts, provider logic, policy checks, and debugging are split across application services with no shared owner.
Instrumentation gets added after the incident
Most teams only start logging model calls after something goes wrong in production — by then, the call that caused it is long gone.
A single latency number hides the real bottleneck
A single total doesn't tell you whether retrieval, guardrails, or the model itself is the slow part — or the part that's wrong.
Production pattern
A direct path from business outcome to callable AI.
LiyaEngine keeps the capability surface simple while the infrastructure behind it remains governed and observable.
01
Own the capability
Create a tenant-owned domain and define the intent contract your product needs.
02
Connect the context
Link knowledge, tools, model configuration, and policies without changing the application interface.
03
Test the behavior
Run representative requests, review structured output, and inspect the full execution path.
04
Operate with evidence
Use traces, evaluations, usage, and cost context to improve the capability after launch.
How LiyaEngine helps
Move infrastructure out of the product code path.
Intent execution can produce a unified trace without each application team building a separate observability layer. Inspect the prompt sent to the model, retrieved context, and guardrail decisions in one place.
Each stage reports its own latency and cost, and every trace gets an ID that's queryable after the fact — reproduce a bad output weeks later without needing to have been watching in real time.
See the technical specTechnical foundation
Read the full Prompt Trace + Diff reference
Full prompt visibility, retrieved-context inspection, guardrail decisions, and per-stage cost/latency breakdown.
Ready to see your first execution trace?
Create your workspace, define a domain, and build the first intent around the outcome your team owns.