dt-obs-genai

name	dt-obs-genai
description	Analyzes observability signals from customer GenAI applications with DQL. Reads OpenTelemetry GenAI spans and LLM evaluation bizevents. Use for: golden signals (traffic, errors, latency, saturation); LLM signals (model, provider, tokens); cost/token analytics, usage attribution, and prompt caching; agent signals (tool calls, steps, failures, loop detection, Smartscape topology); conversation/session analytics; guardrails (blocked/truncated responses); and evaluation signals (quality, pass/fail). Trigger: "LLM latency", "token usage by model", "cost by model and provider", "cost per conversation", "who is driving token spend", "do I have prompt caching", "failing agent tool calls", "find runaway agents", "responses truncated or blocked", "failed evaluations", "am I hitting rate limits", "token throughput / TPM", "provider throttling or 429s". Do NOT use for: Davis CoPilot/MCP telemetry (dt-platform), generic service metrics (dt-obs-services), logs (dt-obs-logs), or non-GenAI tracing (dt-obs-tracing).
license	Apache-2.0

AI Observability (GenAI) Skill

Analyze AI Observability signals from customer GenAI applications using DQL — golden signals, LLM signals, token and cost analytics (with usage attribution and prompt-caching economics), agent signals (including loop/runaway detection and Smartscape topology), conversation/session-level analytics, guardrails, and evaluation quality.

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Core Capabilities

Golden Signals

The four classic observability signals — traffic, errors, latency, and saturation — apply directly to GenAI applications. Traffic is request throughput over time; errors are spans where span.status_code == "error"; latency is the duration field (a Grail duration value — divide by the 1ms literal, duration / 1ms, for a numeric millisecond value); saturation is proxied by total token throughput per minute (input + output tokens combined).

fetch spans, from: now()-24h
| filter isNotNull(gen_ai.request.model)
| summarize total = count(), errors = countIf(span.status_code == "error"), by: {gen_ai.request.model}
| fieldsAdd error_rate_pct = if(total > 0, errors * 100.0 / total, else: 0.0)
| sort error_rate_pct desc

→ Full traffic, latency, and saturation queries: See references/golden-signals.md

LLM Signals

LLM signals describe which model and provider served each request, what operation type was invoked (chat, execute_tool, invoke_agent, create_agent), and how tokens were consumed. Use these to benchmark provider latency, compare model performance, and understand the token distribution across model-provider combinations.

fetch spans, from: now()-24h
| filter isNotNull(gen_ai.request.model)
| summarize p95_ms = percentile(duration, 95) / 1ms, requests = count(), by: {gen_ai.provider.name}
| sort p95_ms desc

→ Slowest models, token usage by model: See references/llm-signals.md

Cost and Tokens

Token consumption is the primary cost driver. Dynatrace stores gen_ai.usage.input_tokens and gen_ai.usage.output_tokens on every span — there is no stored cost field; estimated cost must be derived by multiplying token sums by the per-model price you supply. Use these queries to identify the highest-spend model-provider combinations and detect token-burn spikes.

fetch spans, from: now()-24h
| filter isNotNull(gen_ai.usage.input_tokens) or isNotNull(gen_ai.usage.output_tokens)
| summarize input_tokens = sum(gen_ai.usage.input_tokens), output_tokens = sum(gen_ai.usage.output_tokens), total_tokens = sum(gen_ai.usage.input_tokens) + sum(gen_ai.usage.output_tokens), by: {gen_ai.provider.name, gen_ai.request.model}
| sort total_tokens desc

→ Token spikes, cost estimation, most expensive prompts, usage attribution, prompt-caching economics: See references/cost-and-tokens.md

Agent Signals

GenAI agents emit spans for each tool invocation (execute_tool), agent step (invoke_agent), and agent creation (create_agent). Use agent signals to identify which tools are called most often and which agents are failing. For structural questions — which agents, models, and providers exist and how they connect — query the GenAI Smartscape entities (GENAI_AGENT, GENAI_MODEL, GENAI_PROVIDER, GENAI_SERVICE) instead of scanning spans; this is a feature-flag-gated preview.

fetch spans, from: now()-24h
| filter isNotNull(gen_ai.agent.name)
| summarize total = count(), errors = countIf(span.status_code == "error"), by: {gen_ai.agent.name}
| fieldsAdd error_rate_pct = if(total > 0, errors * 100.0 / total, else: 0.0)
| sort errors desc

→ Tool usage, failing agents, agent step latency, loop/runaway detection, Smartscape topology: See references/agent-signals.md

Conversation Analytics

Per-span and per-trace signals measure one request or one turn. When the application propagates gen_ai.conversation.id, you can roll spans up to the session level — cost per conversation, how deep conversations run, and which sessions are runaway-expensive or error-prone. This is the unit that matters for chargeback and user-perceived reliability.

fetch spans, from: now()-24h
| filter isNotNull(gen_ai.conversation.id)
| summarize turns = countDistinct(trace.id), total_tokens = sum(gen_ai.usage.input_tokens) + sum(gen_ai.usage.output_tokens), errors = countIf(span.status_code == "error"), by: {gen_ai.conversation.id}
| sort total_tokens desc

→ Cost/depth per conversation, session error rate: See references/conversation-analytics.md

Guardrails

Guardrails surface as gen_ai.response.finish_reasons on the span — content_filter means a safety filter blocked or redacted output, length means the response was truncated at the token limit — and as the proactive safety evaluators (prompt-injection, pii-leakage, toxicity, bias) in the evaluation bizevents. Use these to quantify blocked and truncated responses and tie them back to the LLM-judge safety verdicts.

fetch spans, from: now()-24h
| filter isNotNull(gen_ai.response.finish_reasons)
| fieldsAdd finish_reason = gen_ai.response.finish_reasons
| expand finish_reason
| summarize calls = count(), by: {finish_reason, gen_ai.request.model}
| sort calls desc

→ Blocked (content filter), truncated (length), finish-reason breakdown: See references/guardrails.md

Evaluation Quality

Evaluation results are captured as bizevents (not spans) with event.type == "gen_ai.evaluation.result". Each evaluator emits one bizevent per response, carrying the score, pass/fail label, explanation, and the exact Q&A pair. Use evaluation queries to monitor quality dimensions and surface failed responses with the LLM judge's reasoning. Each bizevent also carries the trace.id of the run that produced the evaluated response, so you can pivot from a quality failure to the spans that caused it.

fetch bizevents, from: now()-24h
| filter event.type == "gen_ai.evaluation.result"
| filter gen_ai.evaluation.score.label == "fail"
| fields timestamp, gen_ai.evaluation.name, gen_ai.evaluation.score.value, gen_ai.evaluation.explanation, gen_ai.evaluation.input.question, gen_ai.evaluation.input.answer
| sort timestamp desc

→ Quality scores, failed evaluations, fail rates: See references/evaluations.md

Empty-State Check

When any signal query returns no rows, do not report "no data found" — first confirm whether the application sends GenAI telemetry at all. These two presence checks show which signal families are present:

fetch spans, from: now()-24h
| summarize 
    has_genai = countIf(isNotNull(gen_ai.request.model)), 
    has_tokens = countIf(isNotNull(gen_ai.usage.input_tokens) or isNotNull(gen_ai.usage.output_tokens)), 
    has_agents = countIf(isNotNull(gen_ai.agent.name)), 
    has_tools = countIf(gen_ai.operation.name == "execute_tool"), 
    has_conversation = countIf(isNotNull(gen_ai.conversation.id)), 
    has_finish_reason = countIf(isNotNull(gen_ai.response.finish_reasons)), 
    has_cached_tokens = countIf(isNotNull(gen_ai.usage.cache_read.input_tokens) or isNotNull(gen_ai.usage.cache_creation.input_tokens)),
    total = count()
    

fetch bizevents, from: now()-24h
| filter event.type == "gen_ai.evaluation.result"
| summarize evals = count()

If has_genai is zero, report that the application appears not to be instrumented for AI Observability yet — not "no data found". If has_genai is non-zero but a specific family (has_tokens, has_agents, has_tools, has_conversation, has_finish_reason, has_cached_tokens, evals) is zero, only that signal type is missing — for example has_conversation == 0 means session-level analytics are unavailable because the app does not propagate a conversation id, and has_cached_tokens == 0 means prompt-caching telemetry is not being reported. These optional families may use different attribute names depending on the provider/SDK; verify before reporting them absent.

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Agent Instructions

Act First, Refine Later

When a user asks for analysis, proceed immediately with sensible defaults. Do not ask for parameter values you can reasonably assume.

Default values when not specified:

Parameter	Default	Rationale
Timeframe	Last 24 h (from: now()-24h)	Covers a full operational day without being too narrow
Model scope	All models (no model filter)	Shows the full picture; user can narrow after seeing results
Provider scope	All providers	Same rationale as model scope
Token threshold	None	Show all — let the data reveal the outliers

Exception — cost prices. Per-model prices are the one input you cannot default (there is no cost field in the data). Ask the user for them before estimating USD; never use prices from memory. See cost-and-tokens.md.

Empty-State Rule

When any signal query returns no rows, run the two presence checks in the Empty-State Check capability above before responding — never reply "no data found". If has_genai is zero, report that the application appears not to be instrumented for AI Observability yet; if only a specific family is zero, say which signal type is missing.

Scope Boundary

This skill covers AI Observability signals for customer GenAI applications only. Product documentation and configuration how-to questions (e.g., "How do I configure the Dynatrace OTLP endpoint?") go to ask-dynatrace-docs — this skill does not contain product configuration how-tos.

Understanding User Intent

Map user requests and prompt-starter phrasings to capabilities:

User Request / Prompt Starter	Capability	Reference File
"Understand AI Observability signals"	All signal categories overview	This SKILL.md
"Analyze LLM latency and errors", "LLM errors", "error rate by model"	Golden Signals	golden-signals.md
"Which models are slowest right now?", "compare latency across providers"	LLM Signals	llm-signals.md
"Show token usage by model", "token usage spikes"	Cost and Tokens	cost-and-tokens.md
"Break down cost by model and provider", "which prompts are most expensive?"	Cost and Tokens	cost-and-tokens.md
"Trace a failing agent run", "show failed tool calls"	Agent Signals	agent-signals.md
"Break down agent steps by latency"	Agent Signals	agent-signals.md
"Map agent topology", "which models does this agent use?", "list GenAI agents/models/providers"	Agent Signals (Smartscape)	agent-signals.md
"Is an agent stuck in a loop?", "find runaway agents", "what caused the token spike?"	Agent Signals (loops)	agent-signals.md
"Cost per conversation", "most expensive sessions", "how deep do conversations run?"	Conversation Analytics	conversation-analytics.md
"Stitch together an agent trajectory", "filter by session id", "connect traces across a session"	Conversation Analytics	conversation-analytics.md
"How often are responses blocked/filtered?", "are responses being truncated?", "finish reasons"	Guardrails	guardrails.md
"Cost by application/user/tenant", "who is driving token spend?"	Cost and Tokens (attribution)	cost-and-tokens.md
"Do I have prompt caching?", "cache hit rate", "caching savings"	Cost and Tokens (caching)	cost-and-tokens.md
"Summarize evaluation quality scores", "show low-scoring responses", "show failed evaluations"	Evaluation Quality	evaluations.md
"What signals am I missing?", "why is there no data?"	Empty-State Check	This SKILL.md

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Common Workflows

Workflow: Cost Investigation

1. Run token usage by model and provider (cost-and-tokens.md → "Token usage by model and provider")
2. Identify the top model-provider combinations by total_tokens
3. For the top offenders, run token usage spikes to check for abnormal time windows
4. Use the cost-estimation template in "Most expensive prompts and models" to estimate USD spend — ask the user for per-model prices first (see "Exception — cost prices" under Agent Instructions)
5. Check for prompt-size outliers: high input_tokens / output_tokens ratio indicates large context windows
6. Attribute spend to a consumer (cost-and-tokens.md → "Usage attribution") and check whether prompt caching is enabled and effective (cost-and-tokens.md → "Prompt caching economics")

Workflow: Token-Spike / Runaway Investigation

1. Run token usage spikes (cost-and-tokens.md → "Token usage spikes") to find the abnormal time window
2. Within that window, run repeated-tool-calls and runaway-turn queries (agent-signals.md → "Agent loops and runaway detection")
3. For a flagged trace.id, open the trace to see what the agent looped on
4. If conversation ids are present, re-run the loop query grouped by gen_ai.conversation.id to catch cross-turn loops (conversation-analytics.md)

Workflow: Failing Agent Run

1. Run failing agent activity query (agent-signals.md → "Failing agent activity")
2. Sort by errors desc to find the most error-prone agent
3. Take the trace.id from a failing span and open in Dynatrace distributed-tracing view
4. Check agent steps by latency (agent-signals.md) to see which operation type is slowest

Workflow: Guardrail & Safety Review

1. Run the guardrails presence check (guardrails.md) to confirm finish reasons are recorded
2. Run the finish-reason breakdown, then the blocked (content_filter) and truncated (length) queries
3. Correlate content-filter spikes with the prompt-injection evaluator and truncation with answer-completeness failures (evaluations.md)
4. For a specific block or failure, take the trace.id and pivot to the originating spans (evaluations.md → "Correlating evaluations to traces")

Workflow: Evaluation Review

1. Run evaluation quality scores (evaluations.md → "Evaluation quality scores") to rank evaluators by avg_score asc
2. Focus on the lowest-scoring evaluator
3. Run failed evaluations (evaluations.md → "Failed evaluations") to surface the exact Q&A pairs and LLM judge explanations
4. Use the "Fail rate by evaluator" query to see how many responses fail each evaluator and the share of total evaluations
5. To root-cause a specific failure, take its trace.id and pivot to the originating spans (evaluations.md → "Correlating evaluations to traces")

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References

• references/golden-signals.md — traffic, errors, latency, saturation
• references/llm-signals.md — slowest models, provider latency, token usage by model
• references/cost-and-tokens.md — token usage, spikes, cost-estimation template, usage attribution, prompt-caching economics
• references/agent-signals.md — tool usage, failing agents, step latency, loop/runaway detection, Smartscape agent topology (preview)
• references/conversation-analytics.md — session-level cost, depth, and error rate (gen_ai.conversation.id)
• references/guardrails.md — blocked (content filter) and truncated (length) responses via finish reasons
• references/evaluations.md — quality scores, failed evals, fail-rate, trace correlation (bizevents)