Respan

Use Case 1: Debugging Complex Agent Workflows

Problem: Developers struggle to trace why an AI agent failed or deviated from its expected execution path during multi-step runs.
Solution: Use Respan's end-to-end tracing to log every prompt, tool call, and model response with full execution paths, then replay the exact session in a playground environment to test fixes.
Example: A voice agent platform analyzes a customer call trace to see exactly which tool invocation failed and tests a prompt fix in the playground.

Use Case 2: Standardizing LLM Evaluation Pipelines

Problem: Engineering teams maintain separate pipelines for manual human reviews, automated code checks, and LLM-as-a-judge evaluations.
Solution: Consolidate these evaluation methods into a unified workflow to measure system quality against consistent baseline datasets.
Example: A developer runs synthetic test cases through a combined evaluation pipeline before pushing a new prompt version to production.

Use Case 3: Managing Multi-Model Deployments and Gateway Routing

Problem: Updating prompts and switching between different LLM providers requires modifying application code and rebuilding infrastructure.
Solution: Deploy prompts directly from the UI and route traffic across 500+ models using a single unified gateway with built-in rollout controls.
Example: An engineering team shifts traffic from GPT-4 to Claude for a specific workflow by updating the gateway configuration without redeploying their core service.