AI Observability Engineering

Observability for LLM-Powered Systems

We instrument AI applications with trace-level visibility into model calls, retrieval steps, and agent decisions — from development debugging through production monitoring and compliance audit trails.

Typical engagement starts when

• agent or RAG systems are in production but debugging failures requires reconstructing behavior from scattered logs
• cost attribution is a guess: no breakdown by customer, feature, or model call
• compliance or security teams need decision audit trails the current system cannot produce
• latency and quality regressions ship because there is no evaluation pipeline or alerting on retrieval degradation
• the team knows observability is weak but does not have time to instrument properly while shipping features

What We Build

Engineering Standards

• Semantic conventions for LLM spans: model name, token counts, latency, cost, and prompt/completion hashes
• Span correlation across agent boundaries: trace IDs propagated through tool calls, retrieval, and multi-step workflows
• Cost calculation at instrumentation time: token counts × model pricing captured per span, not reconstructed later
• Sampling strategies for high-volume production: head-based sampling for cost control, tail-based for error capture
• Alert thresholds derived from baseline behavior: latency p99, cost per request, retrieval recall degradation

When to Use This

LangSmith vs. OpenTelemetry

Use LangSmith when the stack is LangChain-native and evaluation/feedback loops are priorities. Use OpenTelemetry when observability must span multiple frameworks or integrate with existing infrastructure.

Common failure patterns we fix

• tracing added post-production with inconsistent span structure, making debugging harder than before
• cost tracking implemented at billing cycle rather than request level, so attribution is always stale
• latency dashboards showing averages instead of percentiles, hiding tail latency problems
• audit logs capturing outputs but not inputs, model versions, or intermediate reasoning steps
• observability instrumentation creating performance overhead that changes the behavior it measures

What you leave with

• trace instrumentation across LLM calls, retrieval, and agent decisions with consistent span structure
• cost attribution dashboards showing spend by customer, feature, model, and time period
• latency monitoring with percentile-based alerting for model calls and end-to-end flows
• compliance-ready audit trails with retention policies and query interfaces
• runbooks for debugging production failures using trace data

Best Fit

• Team has AI systems in production with inadequate visibility into behavior, cost, or latency
• Organization needs compliance audit trails for AI decision-making
• Engineering team is debugging production failures without trace-level visibility
• Cost growth is a concern and attribution is currently guesswork

Depth of Practice

We instrument AI observability across agent orchestration, RAG pipelines, and multi-model routing systems. Production deployments include LangSmith-traced agent workflows processing thousands of daily executions with full cost attribution and compliance audit trails.