Telerik

Production AI debugging

Teams can pinpoint exactly where an agent workflow breaks, from skipped tools to retrieval issues and error loops.

Cost control

Developers track token usage and estimated costs per model, provider, and agent to reduce AI spend.

Quality assurance

LLM-as-a-Judge evaluations measure whether outputs are good enough to ship, catching hallucinations and ungrounded responses.

Workflow optimization

Teams analyze workflow patterns to understand how prompts, models, and tools interact in multi-step agent runs.

RAG system monitoring

Engineers diagnose retrieval issues and bad context that degrade answer quality.

Multi-agent orchestration

Users can see how decisions unfold across multi-step and multi-agent workflows, not just simple request-response paths.

Trace Explorer

Captures the full path of an agent run across prompts, models, tools, retrieval steps, and outputs, measuring spans, model calls, latency, token usage, and more.

Workflow Debugging

Pinpoints where behavior broke down using trace-level context from real agent runs, including errors, failed spans, retries, and workflow status.

Cost Attribution

Tracks token usage and estimated cost tied to execution paths, showing spend by models, providers, agents, and workflow patterns.

Quality Scorecards

Uses LLM-as-a-Judge evaluations to produce quality scores and measure prompt, model, and workflow changes.

Datasets & Experiments

Supports repeatable evaluations connected to production traces, so teams can test changes against real data.

Prompt Management

Lets users manage and iterate on prompts within the observability workflow.

Model Optimization

Provides performance metrics to help teams choose and tune models effectively.

AI Playground

Offers a hands-on space to explore and test AI behavior within the platform.

Data Export

Allows teams to export observability data for further analysis or integration.