Execution timeline
Review the ordered stages that transformed input into a final response.
Platform
Tracing
See the complete path behind every response.
Inspect request input, retrieval, model activity, tool use, guardrail decisions, latency, tokens, and estimated cost in one execution record built for debugging AI behavior.
Execution trace
Ready
TimelineContextPoliciesOutput
Live configuration
00ms request accepted
18ms context retrieved
41ms policy evaluated
... model execution
done response validated
18ms context retrieved
41ms policy evaluated
... model execution
done response validated
StatusCompleted
Stages5
EvidenceAvailable
End-to-endFollow the request across platform stages.
Capability-levelTrace behavior by domain and intent.
OperationalUse evidence to debug, evaluate, and optimize.
Platform capabilities
Everything needed to operate this layer.
Build the capability once, keep its operating context visible, and improve it without spreading AI infrastructure across your application.
Retrieved context
See which knowledge was selected and made available during execution.
Tool activity
Inspect agent tool calls, intermediate results, and failure states.
Policy decisions
Understand which guardrails ran and the evidence behind their outcomes.
Runtime attribution
Connect requests to the provider, model, token usage, latency, and estimated cost.
Debugging evidence
Move from a vague bad response to the specific stage and configuration that produced it.
Operating workflow
From configuration to production evidence.
The interface follows the work in the order teams actually perform it.
01
Find the request
Filter execution history by capability and state.
02
Follow the timeline
Identify the stage where behavior diverged.
03
Inspect evidence
Review context, tools, policy, and model data.
04
Improve the intent
Change configuration and verify the result with another run.
What improves
A cleaner operating model for production AI.
Move complexity into a visible platform boundary while keeping the product integration direct.
01
Faster diagnosis
Teams can isolate retrieval, policy, tool, and model issues without reproducing the whole stack manually.
02
Evidence-led iteration
Prompt and configuration changes are informed by real execution data.
03
Shared operational language
Product, engineering, and governance teams can review the same request record.
Questions
Before you build.
Practical answers about how this part of LiyaEngine fits into the platform.
Do I need to add tracing code to my app?+
LiyaEngine captures the platform execution path for intent calls. Your application can continue using the intent API contract.
What does a trace include?+
Available stages can include request data, retrieval context, tool activity, policy decisions, model attribution, latency, tokens, cost, and output.
Can traces help with evaluations?+
Yes. Trace evidence provides the operational context needed to understand and improve evaluation results.
Build the capability. Keep the operational context.
Start with a tenant-owned domain and expose the first intent through a stable, governed API.