AI Observability Engineering
Production observability for LLM applications: LangSmith, OpenTelemetry, cost tracking, and decision audit trails. We instrument AI systems so you can debug, optimize, and demonstrate compliance.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
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
| Signal | Why Observability Fits |
|---|---|
| Agent or RAG systems are in production but behavior must be reconstructed from scattered logs | Trace structure is missing where debugging actually happens |
| Cost attribution is a guess | The team needs breakdowns by customer, feature, and model call |
| Compliance or security teams need decision audit trails | The current system cannot explain what happened well enough |
| Latency and quality regressions ship unnoticed | Evaluation and alerting are not connected to production behavior |
| Feature work keeps crowding out instrumentation | Observability needs a dedicated engineering pass |
What We Build
| Capability | What We Deliver |
|---|---|
| Trace instrumentation | LangSmith or OpenTelemetry tracing across LLM calls, retrieval steps, tool executions, and agent decisions |
| Cost attribution | Per-request, per-customer, and per-feature cost tracking with model-level breakdown |
| Latency monitoring | p50/p95/p99 latency dashboards for model calls, retrieval, and end-to-end agent execution |
| Audit trails | Immutable decision logs for compliance: inputs, outputs, model versions, and approval states |
Engineering Standards
| Standard | What It Protects |
|---|---|
| Semantic conventions for LLM spans | Model calls, token counts, latency, cost, and prompt/completion hashes stay comparable |
| Span correlation across agent boundaries | Trace IDs survive tool calls, retrieval, and multi-step workflows |
| Cost capture at instrumentation time | Attribution is not reconstructed from billing data after the fact |
| Sampling strategy for production volume | Cost control and error capture are deliberate, not accidental |
| Alert thresholds derived from baseline behavior | Latency, spend, and retrieval quality alerts reflect the actual system |
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| LangChain/LangGraph stack, need integrated tracing and evaluation | LangSmith instrumentation with dataset-driven evaluation |
| Multi-vendor model routing, need unified observability across providers | OpenTelemetry with custom semantic conventions for LLM spans |
| Compliance requires immutable decision audit trails | Structured logging to append-only store with retention policies |
| Cost is growing but you cannot attribute it to customers or features | Cost attribution instrumentation with per-span token tracking |
| Existing Datadog/Prometheus stack, need AI-specific dashboards | Custom metrics and dashboards integrated with existing observability |
| System is early-stage and observability can wait | Minimal logging now; plan instrumentation before production traffic |
LangSmith vs. OpenTelemetry
| Aspect | LangSmith | OpenTelemetry |
|---|---|---|
| Integration | Native LangChain/LangGraph integration | Vendor-agnostic, works across any stack |
| Evaluation | Built-in dataset evaluation, human feedback, A/B testing | Requires external evaluation tooling |
| Cost | Per-trace pricing at scale | Self-hosted or vendor-dependent |
| Best for | LangChain-native stacks, rapid iteration, integrated evaluation | Multi-vendor, multi-framework, existing observability investment |
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
| Pattern | Engineering Fix |
|---|---|
| Tracing added post-production with inconsistent spans | Standardize span naming, metadata, and propagation |
| Cost tracking happens only at billing-cycle level | Capture attribution at request and span level |
| Dashboards show averages and hide tail latency | Track percentile-based latency for model, retrieval, and end-to-end flows |
| Audit logs capture outputs but miss inputs or model versions | Log the evidence needed for later review |
| Instrumentation changes the behavior it measures | Keep overhead visible and sampling intentional |
What you leave with
| Output | Decision It Supports |
|---|---|
| Trace instrumentation | Debug LLM calls, retrieval steps, and agent decisions from one trace surface |
| Cost attribution dashboards | See spend by customer, feature, model, and time period |
| Latency monitoring | Catch model-call and end-to-end flow regressions before they spread |
| Audit trail design | Give compliance and security teams reviewable evidence |
| Debugging runbooks | Make production failure review repeatable |
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 with cost attribution and compliance audit trails.
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