Why Observability Agents Still Can’t Confirm Root Causes Despite Wider Connectors

01 Expand Investigation Radius with 30+ Data Sources

Grafana 13.1 (released 2026‑06‑24) added eight new Assistant‑queryable data sources—Snowflake, Oracle, Elasticsearch, Dynatrace, Honeycomb, Zabbix, Jira and MongoDB. By July 14, Grafana Labs announced that Assistant supports more than 30 sources in total. This expands the Agent’s reach from Prometheus, Loki, Tempo and other telemetry systems to third‑party observability platforms, databases, data warehouses and project‑management tools. As long as a data source is configured in Grafana and the current user can query it, Assistant can read the data via natural language, correlate cross‑source signals, and generate a Grafana dashboard, compressing what previously required multiple pages and manual copying.

02 Data Is Real, Root Cause May Still Be Wrong

Cross‑source access improves clue‑gathering efficiency but does not automatically prove causality. In the illustrated scenario, an error, increased DB latency and a schema migration occur around the same time; the data are all genuine, yet the hypothesis “schema change caused DB slowdown, which triggered the error” remains unverified. Multiple plausible explanations exist—lock contention, cache miss, traffic surge, or resource contention could also cause DB slowdown, and the interface delay might precede the DB call. The observed simultaneous changes only demonstrate correlation.

Three basic errors can be amplified by cross‑source investigation: mismatched time zones, sampling periods, or event precision; inconsistent naming of services, DB instances, or Jira projects; and differing metric definitions (e.g., “error rate” vs “order success rate”). Any misalignment in time, entity, or semantics can lead the Agent to construct an incorrect logical chain from real data.

03 Cross‑Source Query vs Structured Investigation Are Different Capabilities

Assistant’s support for 30+ data sources primarily provides natural‑language querying, cross‑source correlation, and visualization. In contrast, Assistant Investigations offers a longer‑running investigative workflow: it lets users formulate hypotheses, query metrics, logs, traces and profiles, and produces a structured report that records the hypothesis and its source queries. Investigations are currently in public preview.

04 Data Need Not Be Moved, but Entity Relationships and Permission Boundaries Must Be Unified

Grafana’s “Big Tent” architecture does not require enterprises to migrate all data into a single store; logs, metrics, databases, data warehouses and tickets often remain under separate team ownership. However, unified access cannot replace unified semantics. The Agent must know which database instance backs which service, which deployment version corresponds to which release, and how a Jira ticket maps to a production system. OpenTelemetry’s semantic conventions provide identifiers such as service.namespace, service.name, service.instance.id and deployment.environment.name, reducing correlation difficulty but still cannot automatically link services to DB tables, deployments to tickets, or technical metrics to business KPIs.

05 After Connector Competition, the Competition Shifts to Investigation Reliability

Supporting 30+ connectors signals that Agents are moving beyond single telemetry systems toward cross‑system clue collection and hypothesis validation. While this reduces the time engineers spend switching tools, merely aggregating logs, metrics, databases and tickets into one dashboard does not establish a trustworthy causal chain. As Agents gain more access, platforms must add entity mapping, semantic interpretation, execution‑fact verification, counter‑evidence mechanisms, source citations, permission inheritance, and investigation audit trails. Otherwise, “cross‑source intelligence” merely compresses dispersed uncertainty into a more persuasive but potentially incorrect answer.

The next generation of operations platforms will compete not on how many systems they can connect, but on how effectively they can turn cross‑system signals into credible evidence. An Agent’s value lies in producing a verifiable, question‑able, and extensible investigation path rather than a single, seemingly complete conclusion.

Connectors expand the investigation scope; the evidence chain determines the credibility of cross‑system conclusions.