AI Observability
We instrument every agent and LLM call with full tracing, cost, and latency monitoring, so you always know what your systems are doing in production. Real-time performance analytics turn observability into actionable insight.
Book a ConsultationEverything under AI Observability
LLM tracing
Full request-level traces of every prompt, completion, and intermediate reasoning step.
Agent tracing
Step-by-step visibility into agent decisions, tool calls, and multi-agent handoffs in production.
Token/cost monitoring
Granular token usage and spend tracking broken down by agent, workflow, and customer.
Latency monitoring
Real-time latency tracking across the full inference path, with alerting on regressions.
Production monitoring
Always-on health checks and anomaly detection across your deployed agentic systems.
AI performance analytics
Dashboards and reporting that turn raw telemetry into decisions about where to optimize next.
Where AI Observability fits in the end-to-end lifecycle.
- 1
Architecture
Design resilient, scalable agentic system architecture.
- 2
Build
Engineer agents, workflows, and orchestration logic.
- 3
RAG
Ground agents in enterprise knowledge with retrieval pipelines.
- 4
MCP
Connect agents to enterprise tools and systems via MCP.
- 5
Evaluate
Benchmark quality, accuracy, and reliability pre-launch.
- 6
Secure
Harden systems against prompt, data, and access risks.
- 7
Deploy
Ship to production with CI/CD and cloud-native infra.
- 8
Observe
Trace, monitor, and audit agent behavior in real time.
- 9
Optimize
Tune cost, latency, and performance continuously.
- 10
Operate
Run day-to-day operations with autonomous, self-healing systems.
Explore other pillars
Ready to build production-grade agentic AI?
Talk to our team about architecture, evaluation, and production operations.
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