Respan
Proactive observability, evaluation, and gateway platform that helps engineering teams trace, debug, and continuously improve AI agents in production.
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Product Overview
What is Respan?
Respan (formerly Keywords AI) is a unified control plane for AI agent observability and evaluation. It captures full execution traces across every LLM call, tool invocation, routing decision, and memory state β giving teams complete visibility into how their agents actually behave in production. Beyond visibility, Respan closes the loop: it runs automated workflow-level evaluations, surfaces root causes, recommends fixes, and lets teams ship prompt and model changes directly from the platform. Backed by Y Combinator and Gradient, Respan serves teams processing millions of LLM calls per hour, and is compliant with ISO 27001, SOC 2, GDPR, and HIPAA.
Key Features
End-to-End Tracing
Captures 100% of production requests with full span-level detail β inputs, outputs, tool calls, routing decisions, latency, cost, and custom metadata β reconstructed automatically into complete execution trees for multi-step agents.
Automated Evaluation Workflows
Combines code-based checks, human review, and LLM judges in a single evaluation pipeline triggered automatically when prompts, models, or agent behavior changes β no separate tooling required.
AI Evaluation Agent
A first-of-its-kind agent that analyzes failures across trials, localizes root causes to specific decisions, recommends which evals to add next, and intelligently samples live traffic for review.
Prompt & Model Deployment
Version, test, and promote prompts and model changes straight from the UI into production, with rollback support and A/B comparison against prior versions using the same production data.
Adaptive AI Gateway
Single gateway providing access to 500+ models with flexible routing, provider abstraction, BYOK support, and automatic failover β without rebuilding infrastructure.
Real-Time Monitoring & Alerting
Custom dashboards with 80+ graph types track quality, latency, cost, and error rates. Alerts fire via Slack, email, or text when behavior drifts or breaks, with automations to trigger follow-up evals or dataset builds.
Use Cases
- AI Agent Debugging : Engineering teams can open any production trace directly in a replay playground to reproduce failures, test fixes, and confirm resolution without guesswork.
- Regression Protection : Teams shipping frequent prompt or model updates use Respan to run evaluations against historical baselines before every release, catching quality regressions before users are affected.
- Large-Scale LLM Monitoring : Companies processing millions of LLM calls per hour β like voice AI platforms β use Respan's async logging and thread grouping to maintain full visibility across agents, languages, and use cases at scale.
- Dataset Curation from Production : Production traces are automatically converted into labeled evaluation datasets, eliminating the manual effort of building test sets and keeping evaluations grounded in real usage.
- Prompt Optimization : Teams use live production data streams to run automatic prompt engineering directly in the platform, continuously improving agent outputs without offline experimentation cycles.
FAQs
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