AI agent traces in BigQuery turn observability into business data

At a glance

What this is: This is an analysis of how AI agent traces can be queried in BigQuery alongside billing, infrastructure, and customer data, with the key finding that warehouse-native joins make agent behaviour measurable across cost, performance, and business impact.

Why it matters: It matters because IAM, data, and AI governance teams need trace data to be handled like governed business evidence, especially when AI agents, human reviewers, and operational systems all contribute to the same decision chain.

By the numbers:

• Arize says its Data Fabric syncs trace data to the warehouse in approximately 60 minutes.
• Arize reports that adb has demonstrated from 2.7x to 176x better performance than competing analytical systems.
• Arize's example enterprise support agent handles 10k+ sessions monthly across order status checks, refunds, account updates, and escalations.

👉 Read Arize's analysis of querying AI agent traces in BigQuery

Context

AI agent traces become a governance problem when they are treated as disposable telemetry instead of durable records of what an agent saw, decided, and executed. In a production AI environment, the practical challenge is not whether data exists, but whether teams can join that data to billing, infrastructure, and customer records in a controlled way. For identity and access programmes, that makes agent traces part of the evidentiary layer that supports review, investigation, and accountability.

The article sits at the intersection of agentic AI governance, data engineering, and operational control. Its central point is that agent behaviour becomes easier to govern when traces move into a warehouse model, but that also raises questions about access boundaries, retention, and auditability. That is a genuine identity and NHI concern because AI agents, service accounts, and human analysts may all touch the same dataset.

Key questions

Q: How should teams govern AI agent trace data in a warehouse?

A: Treat trace data as governed operational evidence, not throwaway telemetry. Apply retention, role-based access, audit logging, and lineage controls before broad analytics users can query it. If AI agents, service accounts, and analysts all touch the same warehouse, the trace layer needs the same discipline you would apply to customer or billing records.

Q: Why do AI agents create new risk in non-human identity management?

A: AI agents create risk because they operate as software identities with delegated authority, but many organisations do not track them with the same discipline applied to users or service accounts. They can connect quickly, persist across teams, and accumulate permissions that are hard to review. That combination increases the chance of unnoticed access drift and credential exposure.

Q: How do you know if joined AI telemetry is actually working?

A: Look for stable identifiers, reproducible joins, and analysis that answers cost, latency, and business outcome questions without manual stitching. If analysts still export spreadsheets or reconcile mismatched timestamps, the warehouse model is not yet reliable enough to support governance or operational decisions.

Q: What should security teams do before exposing trace data to analysts?

A: Restrict raw trace access, separate human and machine roles, and publish curated reporting tables for common use cases. That reduces unnecessary exposure while preserving the evidence needed for incident review, model evaluation, and cost governance.

Technical breakdown

Why agent traces behave like warehouse tables

AI agent traces are more structured than ordinary logs because they capture session-level decisions, tool calls, token usage, latency, and evaluation scores. That makes them suitable for relational analysis, especially when each span or session can be represented as a row with typed fields such as prompt template, tool name, cost, and score. The practical change is that observability data can be treated as a governed analytical dataset instead of an opaque export.

Practical implication: design trace schemas so they can be queried, governed, and audited like other warehouse tables.

How Iceberg and external tables change the data path

Apache Iceberg separates table metadata from the underlying files, which allows the same trace data to be read by multiple engines without repeated conversion. In this model, Parquet files live in cloud storage and a warehouse such as BigQuery can query them through external tables. That reduces duplication, but it also makes metadata hygiene and access control more important because the warehouse is now a shared access point to operational AI evidence.

Practical implication: apply warehouse access controls and metadata governance before exposing trace datasets to broader analytics teams.

Why joins between traces, billing, and infrastructure matter

The technical value comes from joining agent traces to billing exports and infrastructure metadata, not from any single dataset in isolation. Once those joins exist, teams can separate model cost from infrastructure latency, compare prompt templates, and connect agent behaviour to business outcomes. That is a classic control-and-measurement pattern for AI operations, but it only works when identifiers, timestamps, and retention policies are consistent across systems.

Practical implication: standardise identity fields and timestamps across telemetry, finance, and customer systems before relying on joined analysis.

NHI Mgmt Group analysis

Agent traces are becoming governed business records, not disposable telemetry. Once traces are used for cost analysis, customer impact review, and operational debugging, they inherit the same governance requirements as financial and customer data. That creates an identity boundary problem because access to the trace warehouse may expose sensitive prompts, tool invocations, and evaluator outputs. Practitioners should treat trace datasets as controlled evidence, not analyst convenience data.

Warehouse-native observability improves analysis, but it also concentrates risk. Moving traces into BigQuery and Iceberg makes joins easier, yet it also puts more value behind a smaller number of access paths. That is good for consistency and bad for uncontrolled spread if service accounts, analyst roles, or automation pipelines are over-permissioned. The field should view this as a Zero Standing Privilege and access-minimisation issue, not just a data architecture upgrade.

Context graph thinking creates a new named concept for AI governance debt. A context graph is the linked record of agent decisions, operational events, and business outcomes that turns telemetry into institutional memory. The governance debt appears when organisations collect traces but cannot reliably connect them to costs, outcomes, and accountability. Practitioners should build trace governance so the context graph remains trustworthy, queryable, and reviewable over time.

AI observability now intersects with NHI governance because agent datasets are accessed by non-human actors. Service accounts, pipelines, and AI-native workflows will increasingly query the same trace warehouse used by human analysts. That means authentication, authorisation, lineage, and audit logging matter as much as schema design. Identity teams should expect trace platforms to become part of the NHI control plane, even when the original problem is observability.

This approach validates the shift from event collection to decision evidence. The article shows that mature AI operations depend on retaining the sequence of reasoning, tool use, and business effect, not just raw performance metrics. That aligns with broader governance expectations in NIST AI RMF and OWASP Agentic AI guidance. Practitioners should build around evidence retention and access review, not around isolated dashboards.

What this signals

AI observability will increasingly be judged by whether it can survive governance scrutiny, not just whether it can support debugging. As trace data moves into warehouses, identity teams should expect access review, auditability, and non-human principal management to become part of AI operations rather than a separate control layer.

The practical risk is that a warehouse filled with agent traces can create a false sense of control if the underlying principal model is weak. If service accounts, analyst roles, and automation all share broad access, the system may be analytically rich but operationally fragile.

A useful next step is to align trace governance with the NIST AI Risk Management Framework and the OWASP Top 10 for Agentic Applications 2026, especially where agent actions, data access, and evidence retention converge.

For practitioners

• Define trace data as governed evidence Classify agent traces, evaluations, and annotations as business records with explicit retention, access, and review rules. Tie those rules to the trace warehouse rather than leaving them inside the observability tool alone.
• Review service account access to trace warehouses Audit every non-human principal that can read or write trace tables, external Iceberg metadata, or linked billing datasets. Apply least privilege and isolate analyst access from automation access.
• Standardise identifiers before joining systems Make session IDs, customer IDs, timestamps, and project labels consistent across traces, billing exports, and infrastructure tables. Without aligned identifiers, joins become ambiguous and governance evidence breaks down.
• Materialise high-value subsets for controlled reporting Create curated tables for dashboards and executive reporting rather than exposing raw trace datasets broadly. Use those subsets for recurring metrics so access is narrower and query cost is predictable.

Key takeaways

• AI agent traces become materially more useful when they are queryable alongside billing, infrastructure, and customer data, but that also turns observability into a governance surface.
• The biggest control issue is not storage format, it is whether access to trace evidence is tightly governed across human and non-human principals.
• Teams that want reliable AI decision evidence need consistent identifiers, narrow access paths, and curated reporting layers before they scale warehouse-native trace analysis.

Key terms

• Agent Trace: A structured record of an AI agent’s runtime activity, including model calls, tool calls, approvals, and subagent steps. In practice, traces support debugging, evaluation, and governance when they are retained, searchable, and tied to the permissions behind the agent.
• Context graph: A persistent data layer that links telemetry with organisational knowledge such as asset ownership, tickets, prior investigations, and business workflows. It gives AI systems the context needed to interpret alerts correctly instead of guessing from isolated logs.
• Iceberg Table: An Apache Iceberg table stores metadata separately from the data files so different engines can query the same dataset consistently. In AI operations, that makes it easier to share trace data across warehouses, but it also requires strong governance over who can discover and query the underlying files.
• Warehouse-Native Observability: An observability approach that stores telemetry in a warehouse so it can be joined with billing, infrastructure, and business data using standard SQL. It improves analysis and accountability, but it also turns telemetry into governed business evidence that needs access controls, lineage, and retention policies.

What's in the full article

Arize's full article covers the operational detail this post intentionally leaves for the source:

• Step-by-step SQL examples for joining traces with billing exports, infrastructure metadata, and customer datasets.
• Implementation details for creating BigQuery external tables over Iceberg metadata in cloud storage.
• Performance and cost considerations for partition pruning, materialised subsets, and warehouse query latency.
• Production examples showing how an enterprise support agent's traces map to cost, latency, and customer outcomes.

👉 Arize's full post covers the SQL patterns, Iceberg setup, and business-impact joins in detail.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It helps security practitioners build the access, audit, and lifecycle controls that govern non-human access to operational data.