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 happens next
- 1. Context We review the situation and constraints.
- 2. Fit We recommend an appropriate next step.
- 3. Scope If relevant, we discuss scope.
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.
If tracing is already in place but release tests are missing, AI Evaluation Engineering builds task-specific datasets, graders, and regression checks.
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.
Engineering evidence
Codebase Analysis Agent: 30 Seconds to First Answer
ActiveWizards (Internal)
Language-aware chunking with Tree-sitter, FAISS vector retrieval, and LLM reasoning. 30 seconds from upload to first contextual answer on any codebase.
Competitor Intelligence Agent: Structured Research Workflow
ActiveWizards (Internal)
Multi-agent system for repeatable competitive analysis across pricing, features, and positioning with structured Pydantic-validated output.
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Discuss your AI Observability Engineering path
Tell us about your system, the decision ahead, and the constraints. We will review the context and recommend the next step.
Direct contact with a principal engineer.