Distributed Observability Engine Built for Non-Deterministic AI.
Traditional APMs assume code execution paths are deterministic. AI agents branch dynamically, invoke tools conditionally, and can silently retry until context limits collapse. SynapTrace turns agent execution into deterministic, auditable DAGs.
Live Trace Engine Mockup
Status: Interactive SimulationAgent Orchestrator
type: coordinatorThree pillars of reliable agent operations
1. DAG Trace Ingestion
Every agent reasoning turn, tool argument, SQL/vector database retrieval, and LLM completion is recorded as a linked OpenTelemetry span with strict causal ordering.
- ✓ Sub-agent delegation tree
- ✓ Millisecond latency per step
- ✓ Exact prompt & completion tokens
2. Runtime Semantic Guardrails
Run async assertions across outputs without blocking your primary inference stream. Identify hallucination drift, prompt injections, and invalid tool schema returns.
- ✓ Non-blocking eval pipelines
- ✓ JSON schema compliance validation
- ✓ Hallucination & groundedness scores
3. Circuit Breakers & Cost Caps
Protect your infrastructure budget from runaway agents. Set hard thresholds for maximum turns, max spend per user session, and automated emergency abort triggers.
- ✓ Recursive loop termination
- ✓ Per-tenant budget ceilings
- ✓ Real-time PagerDuty/Slack webhooks
Zero-Payload Storage Architecture
Built for strict enterprise compliance and data sovereignty.
By default, SynapTrace offers a Metadata-Only Mode: your application sends metadata (token counts, latency, tool invocation names, latency percentiles, and boolean assertion outcomes), while raw sensitive customer prompts and completions remain strictly in your own private cloud or VPC.
Currently in Private Developer Alpha
We are onboarding early engineering teams incrementally to ensure zero telemetry disruption.