As LLMs, RAG, and agentic workflows move into core user journeys, APM must expand beyond latency and errors to measure quality, cost, and risk—so teams can ship AI features with confidence.

Why “performance” now includes model behavior

Classic Application Performance Monitoring (APM) cares about service latency, error rates, and resource contention. The minute you add large language models (LLMs), retrieval-augmented generation (RAG), or autonomous agents into checkout, search, or support, the definition of “performance” widens. Suddenly, answer quality, prompt-injection resilience, index freshness, guardrail hits, and token/GPU cost matter as much as p95 response time. If a model replies quickly but hallucinates, you still have a production incident—just a new kind.

This shift has two immediate consequences. First, instrumentation must expose the AI chain end-to-end (prompt build → retrieval → inference → tool calls → post-processing). Second, SLOs must incorporate quality and cost signals, not only latency and availability. “Good” means fast, accurate, safe, and economically viable.

From APM to AI-native APM

AI-native APM keeps all the strengths of traditional APM—distributed tracing, metrics, logs, SLOs—and layers on model-aware visibility:

• Model-aware tracing: Treat prompts, retrievals, and model inference as first-class spans. Include attributes for model ID, token usage, input/output sizes, and policy outcomes.

• Evaluation telemetry: Store per-request quality signals (golden-set accuracy, rubric scores, “model-as-judge” ratings) alongside latency and error counters.

• Safety posture: Record jailbreak attempts, PII exposure flags, toxicity detections, and guardrail violations as events you can alert on.

• Cost-to-serve: Attribute token, GPU, and egress costs to business transactions and tenants. Trigger autoscaling or model tier swaps when cost violates budgets.

• Agent graphs: For tool-using/agentic flows, trace the full call graph (planner → tools → sub-agents) so an on-call can see why a step occurred and where it went wrong.

A practical trace model for AI systems

An AI-aware trace generally has five layers:

1. User intent – The initiating action (search, support ticket, personalization). Store anonymized hints (channel, locale, device class) and the prompt template version.

2. Retrieval – Capture vector lookups or keyword queries, result counts, confidence scores, and data lineage (index version, region, last refresh).

3. Inference – Create spans for every model call with latency, token counts, model identifier, temperature, and safety outcomes.

4. Tooling/agents – Represent tool calls (SQL, HTTP, function execution) and multi-agent hops as nested spans, so developers can reason about orchestration.

5. Post-processing & delivery – Validate schema, run redaction, write caches, and connect the trace to downstream KPIs (conversion, CSAT, containment).

With this structure, on-call engineers can jump from a poor response to the precise root cause: stale retrieval, a slow model region, a policy block, or a brittle prompt template.

Open standards keep you portable

The OpenTelemetry (OTel) community is defining semantic conventions for generative AI: a shared vocabulary for prompts, models, tools, and evaluations. That means you can “instrument once” and route the same signals to any OTel-compatible backend. Pair those conventions with the OpenTelemetry Collector as your routing brain: redact PII at ingest, drop high-cardinality attributes, enrich with business tags (tenant, plan), and fan-out to multiple APM/observability tools without touching app code. Portability matters in a fast-moving market where you may trial several analysis backends before standardizing.

Making quality measurable (and actionable)

If you can’t measure answer quality, you can’t operate AI in production. Practical patterns:

• Golden sets & rubrics: Maintain task-specific evaluation sets. Compute rubric scores (helpfulness, accuracy, safety) continuously and store them as time-series metrics.

• Shadow & canary evals: For every deploy (prompt or model), run both shadow traffic and targeted canaries. Advance only if quality SLOs hold (e.g., “p75 accuracy ≥ 0.85”).

• User feedback as telemetry: Capture thumbs-up/down, CSAT, or hand-off outcomes and attach them to traces. Feedback becomes a training and rollback signal.

• Guardrail analytics: Don’t just block; measure. Which policies fire most often? In which markets or intents? Use that to harden prompts and tooling.

Cost control without losing insight

AI features can silently blow up budgets. Treat cost like latency: budget it, monitor it, and alert. Tactics that work:

• Tail-based sampling on traces so you retain rare, high-value failures without storing every success.

• Caching for frequent prompts and retrievals; record cache hit rates as first-class metrics.

• Tiered models with policy-driven fallbacks (e.g., “use small model unless SLO is at risk or confidence < threshold”).

• Cardinality governance in attributes (hash or bucket user identifiers; avoid logging secrets; bound “free-form” tags).

Incident response for AI features

Runbooks need new moves:

• Prompt/model rollback: Treat prompts like code—version them, gate with canaries, and roll back quickly.

• Retrieval index rebuilds: Automate refresh on drift detectors; escalate if eval scores fall in a specific region/tenant.

• Policy tuning: When guardrails over-block, capture examples in an allowlist review process with audit trails.

• Cross-discipline swarming: Incidents now blend SRE, ML, data, and product. Your dashboards should show one shared narrative from user impact → AI spans → infra/resource hotspots.

Organizational pitfalls (and how to dodge them)

• Black-box AI: If your APM stops at the gateway, you’ll never know why responses degrade. Model-aware tracing is mandatory.

• Metric sprawl: Start with a lean AI scorecard: p95 latency, answer quality p75, safety violations/1k interactions, and cost/interaction. Add gradually.

• Siloed ownership: Assign a single service owner for the end-to-end AI flow, not just the model or the UI.

• Security gaps: Treat prompt logs like production data—access controls, retention limits, purpose tags, and encryption everywhere.

What “good” looks like by the end of 2025

Mature teams run AI features as tier-1 services with quality SLOs alongside latency and error budgets; they use OTel semantics, a governed Collector pipeline, and dashboards that correlate user outcomes with AI spans and infrastructure. In postmortems, they quantify business impact (“2.3% of EU searches missed acceptance criteria for 24 minutes due to index drift”) and list preventive actions (automated index freshness checks, prompt linting CI, shadow eval gates).

Closing Thoughts

AI-native APM isn’t a new tool category—it’s the inevitable evolution of performance engineering for probabilistic software. By tracing prompts, retrieval, inference, and agent actions—and budgeting quality and cost next to latency—you replace guesswork with observability. The result is safer launches, faster incident response, and a tighter line from AI investment to measurable outcomes.

Reference sites (5)

Publication: OpenTelemetry (Specs)
Topic: Semantic Conventions for Generative AI
URL: https://opentelemetry.io/docs/specs/semconv/gen-ai/

Publication: Datadog (Press Release)
Topic: Datadog Expands LLM Observability with New Capabilities to Monitor Agentic AI
URL: https://www.datadoghq.com/about/latest-news/press-releases/datadog-expands-llm-observability-with-new-capabilities-to-monitor-agentic-ai-accelerate-development-and-improve-model-performance/

Publication: New Relic (Product Blog)
Topic: Introducing New Relic AI Monitoring — End-to-End Visibility for AI-Powered Apps
URL: https://newrelic.com/blog/how-to-relic/ai-monitoring

Publication: Economic Times (ETtech)
Topic: Observability Now Equals Watching AI — Interview with Datadog CPO
URL: https://economictimes.indiatimes.com/tech/artificial-intelligence/observability-now-equals-watching-ai/articleshow/123371124.cms

Publication: OpenTelemetry (Blog)
Topic: AI Agent Observability — Evolving Standards and Best Practices
URL: https://opentelemetry.io/blog/2025/ai-agent-observability/

Author: Serge Boudreaux — AI Hardware Technologies, Montreal, Quebec
Co-Editor: Peter Jonathan Wilcheck — Miami, Florida