Runtime control plane for AI agents .
Surface hidden agent failures and cost issues in real time with no agent code changes required.
Agents fail silently in production• Tools report success but behavior stall
• Agents loop and retry without convergence
• Costs explode with no guardrails
Why this is hardTraditional observability tools capture logs and latency — but they cannot detect semantic AI failures like looping, retry storms, , tool stalls context loss, or cost explosion — because they only happen at the semantic level, not transport levelThey cannot be detected by logs, metrics, or tracing alone.
How it worksApplication / Agent
→ Runtime Interceptor
→ Failure Detection
→ Severity & Recommendations
Works with any LLM provider and agent framework
Who is this forBuilt for: Platform teams, infrastructure engineers, and AI reliability leads who deploy AI agents in production.
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