Observability & AI
See Everything. Understand Everything. Operate AI with Confidence.
Modern systems are no longer just microservices—they are LLMs, agents, tools, retrieval layers, embeddings, and orchestrators working together. Without deep observability, teams can't understand how agents behave, why models respond the way they do, or why costs spike unpredictably.
AVM's Observability & AI practice provides full-stack visibility across infrastructure, data pipelines, LLM workloads, RAG systems, and agentic workflows, built on your Data, Cloud, and AI Foundations.
We combine Datadog LLM Observability, Managed Evaluations, and our own AI Foundation pillars to help organizations safely scale production AI.
LLM & AI Observability
We extend classic metrics, logs, and traces with LLM-aware observability, enabling full insight across model and agent behavior.
What We Monitor
- LLM Request Traces Every prompt–response pair captured as a trace with latency, tokens, model version, input/output, and metadata. (Datadog automatically instruments Bedrock, OpenAI, Anthropic).
- Agent Workflow Tracing Step-by-step visibility into agent planning, tool calls, retries, memory, and knowledge-base lookups. You can see every internal decision the agent made.
- RAG Observability Tracks chunking strategy, retrieved documents, vector search behavior, rerankers, and context windows—linked to each LLM span. ("Why did the model say this?" becomes answerable.)
- Operational Health Unified dashboards for throughput, latency, cost, errors, abandonment rate, concurrency, and token spikes. Correlated to infrastructure and application metrics.
- User & Business Impact Connect LLM spans to user actions and KPIs—CSAT, conversion, resolution rate, time-to-answer—directly inside Datadog.
Our Approach
- AVM uses Datadog LLM Observability combined with our AI Foundation pillar: LLM Observability & Evaluation.
- Standardized Instrumentation We ship opinionated patterns for OpenAI, Bedrock, Anthropic, Azure OpenAI, custom gateways, and tool-call frameworks.
- Golden Traces & Golden Datasets We define canonical workflows—"golden journeys"—and ensure they are always monitored for regressions.
- Correlation Across the Stack LLM traces link to upstream APIs, data pipelines, storage engines, caches, and downstream services. No more debugging in isolation.
- Governed Logging & Sampling PII redaction, sampling rules, retention, access control, and cost controls align with your Data & Cloud Foundations.
Deliverables
- LLM-aware instrumentation for your application stack
- Dashboards for AI Health, Performance, Errors, Cost, and Throughput
- SLOs & incident playbooks for LLM/agent failures
- KPI framework: hallucination rate, grounded response rate, regression detection, time-to-debug, cost per task
- End-to-end traceability across agentic workflows, RAG, and tool-calling systems
Managed Evaluations & AI Quality
Datadog's Managed Evaluations provide automated, scalable grading of LLM output quality, safety, and agent correctness—attached directly to traces. These evaluations use LLMs themselves to judge model behavior using structured metrics.
What We Evaluate
- AVM maps Datadog's evaluation suite to your domains:
- Agent Correctness Did the agent achieve the user's goal? Tool selection correctness, tool argument validation, planning quality, goal/success completion evaluation.
- Quality & Safety Did the model respond appropriately? Topic relevance / on-domain responses, hallucination detection (context mismatch), toxicity / policy violations, failure-to-answer / missing content, tone, sentiment, style consistency.
- Contextual & RAG Evaluations Grounded answer rate, citation completeness, retrieval quality & freshness, re-ranking performance.
- All evaluations attach scores, labels, and metadata to the original LLM spans so teams can trace issues back to specific inputs.
How We Implement Managed Evaluations
- Bring-Your-Own-LLM Providers Connect your OpenAI, Anthropic, Bedrock, or Azure OpenAI API keys for evaluation execution. (Supports GPT-4o mini, Claude Haiku, etc.)
- Targeted Sampling & Traffic Selection Run evaluations on specific endpoints, tags, model versions, or traffic percentages. (Avoid cost explosions while maintaining high signal.)
- Token & Cost Tracking Datadog automatically tracks evaluation token usage and cost per evaluation class. Critical for LLM FinOps.
- Dashboards & Alerts Alert on quality drops ("hallucination rate ↑"), agent failures, or safety violations—before users notice. Integrated with Datadog monitors.
- Custom Evaluations (LLM-as-a-Judge) AVM builds natural-language grading rubrics tied to branding, compliance, legal rules, domain knowledge, or gameplay. Extends managed evaluation beyond generic metrics.
Deliverables
- Evaluation catalog aligned to your use cases
- Version-controlled evaluation configs (providers, sampling, scoring rules)
- Dashboards for quality, safety, factuality, grounding, success rates
- Continuous-improvement loop: Detect » Analyze » Experiment » Approve » Deploy
- Part of your LLMOps lifecycle under AI Foundations
Observability Foundations Across Data, Cloud & AI
Everything AVM does in observability is built on your core Foundations: Data Foundations — ingestion, storage, semantics, governance, lineage, data quality. Cloud Foundations — compute, storage, networking, DevOps, security, FinOps. AI Foundations — RAG, agents, embeddings, routing, SLMs, safety, LLM FinOps, serving, LLMOps lifecycle. Observability is not an isolated capability—it is a cross-cutting layer that keeps your entire AI system trustworthy, debuggable, efficient, and safe.
Data & Cloud Observability
- AVM extends your foundations with:
- Full visibility into ingestion, transformation, storage, and query engines
- Tracing for streaming pipelines, CDC, batch ETL, and warehouse queries
- Kubernetes, serverless, API gateway, and multi-cloud observability
- Governance for logs, telemetry cost, sampling, and access control
- FinOps for data & compute telemetry volume
AI Observability & Evaluation
- Our AI Foundations add the AI-specific layer on top:
- LLM Observability & Managed Evaluations (Datadog)
- Agent workflow visibility (tool calls, retries, memory, branching)
- RAG observability (retrieval quality, context drift, grounding metrics)
- LLM FinOps (token budgets, routing, caching, model economics)
- LLMOps lifecycle governance (prompts, datasets, policies, approvals)
- Safety & policy enforcement monitoring
Deliverables
- Reference architecture: AWS + Datadog + Databricks + Bedrock
- Implementation plan for onboarding services & AI workloads
- Enforced observability standards across engineering, security, data, and AI teams
- Documentation & runbooks: alerts, on-call, SLOs, incident response
- Turnkey dashboards for both system health and AI quality
