The next major improvement in observability is moving intelligence closer to where telemetry is created. Filtering noisy records, adding useful context and routing each signal before downstream ingestion can lower processing volume while making incidents easier to investigate. The goal is not to collect everything; it is to collect data that people and automated systems can trust.
Why observability is shifting upstream
Observability platforms traditionally accepted large volumes of logs, metrics, traces and events, then left downstream systems to index, correlate and interpret them. That model becomes harder to sustain as applications spread across clouds, containers, regions and vendors. Fragmented tools produce inconsistent signals, storage and processing costs rise, and responders spend more time separating useful evidence from noise.
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Telemetry is the raw material of observability: the measurements and records emitted by software and infrastructure. Its value depends on relevance, quality and context. A larger data set can still produce weaker answers if it contains duplicates, low-value events or records that cannot be tied to a service, deployment or user request.
Mala Pillutla, Vice President of Sales for Log Management at Dynatrace, captures the change in emphasis: “The advantage will not come from simply collecting more. It will come from ensuring the telemetry an organization does collect is relevant, trustworthy and ready to support action.”
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What an upstream telemetry pipeline does
An upstream-aware design processes signals before they reach analytics and monitoring back ends. A real-time pipeline normally performs three jobs:
Filter low-value data
Rules remove or reduce telemetry that does not support an operational, security or business decision. Filtering can target repetitive events, irrelevant environments or records that duplicate higher-value signals. Policies should be explicit: discarding data without knowing which investigations depend on it can create blind spots.
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Enrich useful signals
Enrichment attaches context that downstream users and tools need to interpret a signal. Examples include service and environment identity, deployment information, region, tenant, ownership and relationships between a trace and its logs or metrics. Context makes a record actionable instead of merely searchable.
Route each signal to the right destination
Routing sends meaningful telemetry to the systems that need it, rather than copying every signal to every platform. A security workflow, incident system and long-term analytics store may require different subsets or formats. Routing early limits unnecessary ingestion and helps teams preserve the distinctions between operational, security and business use cases.
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How the main observability approaches differ
The following comparison describes design characteristics, not a performance benchmark. Actual results depend on deployment, rules and the back-end systems selected.
| Approach | Processing location | Filtering and enrichment | Interoperability | Cost and context implications | Automation readiness |
|---|---|---|---|---|---|
| Downstream-first | Mostly after ingestion | Controls are concentrated in analytics or monitoring tools | Can become tied to each platform’s data model | More raw data may be stored and processed; context may arrive late or inconsistently | Automation inherits the quality and completeness of downstream data |
| Upstream-aware pipeline | Before downstream ingestion | Filters, enriches and routes signals at collection or processing points | Can feed several destinations when formats and policies are managed consistently | Potentially lowers downstream volume and improves the context available for investigation | Produces cleaner inputs for correlation and automated decisions |
| OpenTelemetry-centered pipeline | Collection and processing can be distributed across the telemetry path | Uses a common collection approach while leaving processing and destination choices to the implementation | Open standard designed for complex, multi-vendor environments | Can reduce dependence on one vendor’s collection method; savings depend on filtering and routing policy | Standardized, contextual signals are better suited to cross-tool analysis and AI workflows |
Why OpenTelemetry matters
OpenTelemetry provides an open way to collect telemetry consistently across applications and infrastructure. That matters when an organization operates multiple clouds, languages, platforms or observability products. A common collection layer reduces the need to instrument every system around a single vendor’s format and makes it easier to change destinations over time.
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OpenTelemetry does not, by itself, decide which records are valuable or guarantee that every signal has the context an investigation requires. Teams still need processing policies, ownership rules and destinations. Its contribution is interoperability: the same broad collection strategy can support logs, metrics and traces across a heterogeneous environment, while events and other signals can be handled according to the implementation’s capabilities.
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AI-assisted operations increase the consequences of poor telemetry. An automated system needs accurate, contextualized evidence to explain an anomaly, connect symptoms across services or recommend remediation. Incomplete records, conflicting identifiers and large amounts of irrelevant data make investigations slower and explanations harder to verify. That weakens confidence among engineers and business leaders, even when an AI suggestion happens to be correct.
New Relic’s 2026 announcement illustrates how vendors are exposing observability data for this emerging workflow. Its Ground Truth capability provides observability data through APIs for custom AI agents, while Autopilot is described as an automated SRE agent. These are vendor examples, not proof that automation is reliable without governance. The quality of the telemetry made available to an agent remains a limiting factor.
A practical rollout plan
- Start with decisions, not data volume. List the incident, capacity, security and customer questions the organization must answer. Identify which logs, metrics, traces and events actually support each decision.
- Map the telemetry lifecycle. Document where signals originate, where they are collected, where processing occurs and which systems receive them. Look for duplicated ingestion, missing identifiers and destinations that receive data they never use.
- Define filtering rules with owners. Classify signals as essential, useful for limited investigations or low value. Assign a team to review rules so that cost reduction does not quietly remove evidence needed for compliance or rare failures.
- Add context before correlation. Standardize service, environment, deployment and ownership fields, and preserve relationships among logs, metrics and traces. Context should be attached consistently rather than reconstructed separately in every back end.
- Route by purpose. Send each class of telemetry to the operational, security, analytics or archival system that needs it. Avoid an assumption that every destination should receive every signal.
- Measure quality as well as volume. Track whether responders can find the right evidence, how often records lack required context, and how much data is processed and stored downstream. A smaller data set is not an improvement if diagnosis becomes less reliable.
- Test automation under review. Let AI-assisted tools explain their evidence and keep human approval for consequential remediation until signal coverage, context and failure handling are proven.
Common failure modes
- Filtering for savings alone: aggressive reduction can remove the rare event that explains a major incident.
- Inconsistent enrichment: different teams may use different service or environment names, preventing reliable correlation.
- Processing too late: if every destination receives raw data first, the organization pays the storage and ingestion cost before any reduction occurs.
- Assuming an open standard solves governance: OpenTelemetry improves collection interoperability, but teams still need ownership, retention and routing policies.
- Treating AI output as self-validating: an agent can be fast and confident while relying on incomplete or misleading telemetry.
What the available adoption signal shows
A TechRadar Pro publication page retrieved in 2026 says that 87% of organizations claim to use logs as part of their observability solutions. The page does not identify the original research organization or the year of the underlying study, so the figure should be read as a reported industry signal rather than an independently verified primary benchmark. It does, however, underline why log quality and routing remain central concerns: logs are widely collected, yet collection alone does not establish useful observability.
The direction of observability
Observability is moving from a collection-maximization mindset toward a signal-quality discipline. Upstream filtering, enrichment and routing can make telemetry more relevant before it reaches expensive downstream systems; OpenTelemetry can provide a consistent collection foundation across vendors; and AI initiatives make trustworthy context more important than ever. Teams that compare architectures by signal quality, processing location, interoperability, cost, coverage, explainability and automation readiness will be better positioned than teams that measure success only by how much data they ingest.
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