Agentic AI makes telemetry pipeline architecture the cost factor

At a glance

What this is: NHI Mgmt Group's analysis, drawing on Apica's research: Agentic AI is driving telemetry growth that makes pipeline architecture the deciding factor in whether observability remains affordable and operationally viable.

Why it matters: IAM, NHI, and AI governance leaders need to account for the telemetry burden of autonomous systems because monitoring, auditability, and cost control become inseparable at scale.

By the numbers:

• 54% of enterprises saw their telemetry data volume triple in the past 12 months.
• AI and machine learning workloads now account for approximately 43% of that expansion.
• 69% of agentic AI projects already have observability costs that exceed compute and infrastructure costs combined.
• The average enterprise spends $3.17M annually on observability.

Context

Telemetry pipelines are the routing and control layer that moves logs, metrics, traces and event streams into the systems that analyse them. When agentic AI expands activity volume faster than those pipelines can filter, enrich and govern it, observability becomes a capacity and cost problem rather than a visibility advantage.

The core governance gap is not that organisations lack observability tools. It is that they often treat telemetry collection as a downstream storage problem instead of an architectural control point for scale, cost and auditability. That gap matters for AI agent observability as well as for the identity and access events produced when agents act on production systems.

Apica's findings point to a familiar pattern in infrastructure modernisation: the teams that scale first are usually the ones that redesigned the data path before the demand spike arrived.

Key questions

Q: How do teams size telemetry pipelines for agentic AI growth?

A: Teams size telemetry pipelines by estimating event volume, cardinality, and burstiness from agents, then setting ingest, buffering, indexing, and storage limits to absorb peaks. Common controls include adaptive sampling, tiered retention, backpressure, and schema pruning so logs, traces, and metrics stay searchable without runaway cost or dropped evidence.

Q: Why do autonomous systems drive observability cost so quickly?

A: Autonomous systems drive observability cost quickly because they generate high-frequency logs, traces, and tool-call events from every runtime decision, retry, and failure path. Cost rises when cardinality explodes, because each agent, workflow, prompt, and tool invocation can create distinct telemetry that must be ingested, indexed, retained, and correlated.

Q: How does telemetry routing improve agent governance?

A: Telemetry routing improves agent governance by sending the right events to the right control plane, so approvals, tool use, policy violations, and privilege changes are easier to review. When logs carry identity-linked evidence, teams can tie each action to the agent, its credentials, and the accountable owner for audit and revocation.

Q: What is the difference between sampling and retention in observability?

A: Sampling decides which telemetry events are collected or kept for analysis, usually by dropping a proportion of logs, traces, or spans at ingest or during processing. Retention is how long stored telemetry is preserved before deletion, so it controls historical depth, compliance hold periods, and the amount of searchable evidence available.

Technical breakdown

Why telemetry pipelines become the control plane for observability

A telemetry pipeline sits between data producers and the observability stack. It can route, sample, enrich, compress and suppress data before it reaches expensive downstream storage or analytics systems. That matters when agentic AI increases event volume, because raw ingestion at full fidelity is often neither affordable nor operationally useful. The practical question is no longer how much data can be collected, but where governance occurs in the data path and which signals are preserved for incident response, audit and model oversight. See the AI Agent Observability, Audit and Incident Response Guide.

Practical implication: Move filtering and routing earlier in the telemetry path so high-volume agent activity does not overwhelm downstream monitoring and retention.

Why agentic AI drives a telemetry cost cliff

Agentic systems produce more than routine application logs. They generate decision trails, tool-use events, retries, context changes and downstream system interactions, often at a rate that multiplies quickly with scale. Because those events are distributed across infrastructure, applications and identity controls, the cost grows across collection, transport, storage and search. The result is a cost cliff: visibility requirements rise as autonomy increases, but traditional observability pricing models are often linear only on paper and exponential in practice once retention and query demands are added.

Practical implication: Design for selective retention and content-aware routing so agent telemetry stays usable without forcing every event into premium storage.

How telemetry routing affects identity and agent governance

Agent telemetry is not just infrastructure noise. It is evidence of how non-human identities authenticate, request access and interact with production tools. If that evidence is discarded, summarised too aggressively or stored in ways that cannot support forensics, the organisation loses the ability to attribute agent behaviour or prove control effectiveness. That creates a governance problem for both NHI and agentic AI programmes, because access decisions and runtime actions need a durable, queryable trail. See the Agentic AI Identity Guide.

Practical implication: Preserve identity-linked telemetry for agent actions so access review, incident response and accountability can still be performed at runtime scale.

NHI Mgmt Group analysis

Telemetry debt is becoming governance debt: once agentic workloads make observability unaffordable, organisations start pruning the very evidence they need for control. That creates a blind spot across incident response, audit and service accountability, because visibility is degraded not by attacker action but by architecture choices. The practitioner conclusion is simple: telemetry design now belongs in the same governance conversation as access control and logging policy.

Pipeline-first architecture is the named concept here: the study shows that the organisations most prepared for agentic scale are not the ones with the most observability spend, but the ones that control data flow before it reaches the expensive layers. This is an architectural decision about where routing, filtering and enrichment occur, not a product preference. The practitioner conclusion is to evaluate pipeline capability as a prerequisite for scalable monitoring, not as an optimisation after deployment.

Agentic AI changes the meaning of “enough monitoring”: when autonomous systems generate order-of-magnitude more telemetry, the old assumption that full-fidelity collection is always desirable stops holding. The relevant decision becomes which signals are necessary for attribution, risk detection and compliance, and which can be sampled or transformed without losing assurance. The practitioner conclusion is to define evidence tiers for agent activity before volume forces an unplanned trade-off.

Identity evidence has to survive the telemetry filter: agent actions are increasingly identity events as much as operational events, especially when non-human identities call production tools or services. If logs cannot preserve the who, what and when of those actions, NHI governance loses operational force. The practitioner conclusion is to treat agent telemetry as part of identity control evidence, not merely as platform noise.

Observability cost is now an enterprise risk variable: the point is not that monitoring is expensive, but that budget pressure can force under-monitoring of the very workloads that introduce the most uncertainty. That shifts the discussion from tooling efficiency to resilience and assurance. The practitioner conclusion is to make telemetry economics visible in risk reporting before it constrains deployment decisions.

From our research library:

• 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
• 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
• Read next: Agentic AI Identity Maturity Model

What this signals

Pipeline-first telemetry design will become a standard requirement for agentic programmes: 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey. For practitioners, that means telemetry architecture, evidence retention and access attribution need to be planned together rather than managed as separate operational layers.

Agentic scale will force tighter control over what gets observed and why: 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey. That increases the pressure to preserve high-value identity and action signals while reducing unnecessary data movement and storage.

Telemetry economics will shape deployment decisions, not just monitoring design: 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey. For practitioners, the lesson is that governance debt in one layer often reappears as observability debt in another.

For practitioners

• Audit telemetry architecture against projected agentic growth Measure current ingestion, routing, storage and search capacity against plausible agentic AI growth scenarios, then identify where the architecture fails first under 6x, 9.5x and extreme-volume conditions.
• Separate evidence tiers from raw telemetry volume Define which agent events must be retained at full fidelity for audit and attribution, which can be sampled, and which can be transformed before storage.
• Treat observability cost as a risk metric Report monitoring spend alongside deployment risk when agent telemetry growth threatens to force reduced retention, delayed monitoring or selective blind spots.
• Preserve identity-linked agent logs Ensure non-human identity actions remain traceable through routing, filtering and retention so incident response can reconstruct who acted, what they touched and when.

Key takeaways

• Agentic AI is turning telemetry into a scale and governance problem because the data path itself becomes a limiting control.
• The evidence shows triple-digit or multi-factor growth in telemetry can arrive faster than legacy observability budgets and storage assumptions can absorb.
• Practitioners need pipeline-first designs, identity-linked evidence retention and explicit observability risk reporting before autonomous workloads expand further.

Related NHI Mgmt Group resources: Agentic AI Identity Guide · Agentic AI Identity Maturity Model · AI Agent Observability, Audit and Incident Response Guide · Shadow AI and AI Agent Discovery Guide

Key terms

• Telemetry pipeline: A telemetry pipeline is the path security data follows from collection to analysis and retention. In mature environments it must preserve context, maintain throughput, and avoid introducing blind spots as sources, formats, and volumes change over time.
• Agentic AI: Autonomous AI systems capable of planning, deciding, and taking actions, including calling APIs, writing code, and orchestrating other agents, with minimal human oversight. Agentic AI introduces new NHI risks as agents must authenticate to external services.
• Observability: Observability is the ability to understand the internal state of a system from the data it produces. In security and operations, that means combining logs, metrics, and traces so teams can explain why something happened, not just confirm that something changed.
• Non-Human Identity (NHI): A digital identity assigned to a non-human entity such as a software application, service account, API key, bot, machine, or AI agent that enables it to authenticate and interact with systems without direct human involvement. NHIs now outnumber human identities in most enterprises by 25 to 50 times.

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