They treat it as a binary choice when the real decision is about the right control for the asset and use case. Some environments need low-latency endpoint context, while others can rely on lighter-weight collection. The mistake is forcing one mechanism everywhere and then compensating with more tools, more noise, and more operational overhead.
Why This Matters for Security Teams
Agentless and agent-based telemetry are often discussed as if one is inherently modern and the other is legacy, but the real issue is control fidelity. Security teams need to know what happened, where it happened, and whether the signal is trustworthy enough to support detection, investigation, and response. In agentic AI and broader endpoint security contexts, that question matters even more because an autonomous system can generate actions, not just observations.
The wrong choice can leave blind spots in high-risk assets or create operational drag from redundant collection. Guidance from the NIST AI Risk Management Framework is useful here because it frames telemetry as part of a broader risk and governance model, not a standalone tooling decision. The same logic applies when telemetry supports AI systems, privileged workflows, or other sensitive workloads: the collection method must match the asset’s risk profile, latency needs, and assurance requirements.
In practice, many security teams encounter telemetry gaps only after an investigation has already stalled, rather than through intentional control design.
How It Works in Practice
Agentless telemetry typically relies on platform APIs, cloud logs, hypervisor feeds, network sensors, or SaaS event streams. It is attractive because it reduces endpoint overhead and can scale quickly across heterogeneous estates. Agent-based telemetry, by contrast, installs software on the workload or endpoint to collect deeper context such as process activity, file changes, local user behavior, memory indicators, or execution lineage. Each model has a different trust boundary, coverage profile, and failure mode.
For most organisations, the practical question is not which model is “better,” but which control plane needs which evidence. A cloud control room may be adequately monitored through API-driven logs, while an engineer laptop, privileged admin host, or AI orchestration node often needs richer host-level detail. This is especially important where agentic systems have execution authority and tool access, because telemetry must support both security monitoring and accountability for actions taken. The OWASP Top 10 for Agentic Applications 2026 and the MITRE ATLAS adversarial AI threat matrix are both helpful reminders that AI systems need telemetry that can support abuse detection, tool-use analysis, and post-incident reconstruction.
- Use agentless collection where the platform already emits high-quality, tamper-resistant logs.
- Use agent-based collection where local process, command, or memory context is needed for detection.
- Correlate both streams in SIEM or XDR so analysts can verify activity across layers.
- Define retention, normalisation, and time synchronisation requirements before deployment.
Current best practice is to mix both approaches by asset class, privilege level, and investigation need, then validate the design with tabletop incident scenarios. These controls tend to break down when legacy endpoints, air-gapped systems, or heavily virtualised environments prevent reliable local instrumentation because the resulting telemetry becomes incomplete or inconsistent.
Common Variations and Edge Cases
Tighter telemetry coverage often increases cost, privacy review burden, and operational complexity, requiring organisations to balance visibility against performance and governance constraints.
There is no universal standard for this yet, especially for agentic AI and other autonomous systems where telemetry may capture prompts, tool calls, outputs, and intermediate reasoning artefacts. Best practice is evolving toward selective collection with explicit purpose limitation, but that must be tested against regulatory, legal, and data minimisation requirements. The CSA MAESTRO agentic AI threat modeling framework is useful for thinking about where observability is needed without over-collecting sensitive content.
Edge cases matter. Highly regulated environments may prefer agentless telemetry for sensitive desktops to avoid invasive local software, while high-risk engineering or admin systems may demand agents because API logs alone do not show lateral movement or misuse of local secrets. That tradeoff becomes sharper where telemetry intersects with non-human identities, service accounts, and automation pipelines, because the question is not just what was accessed, but which identity actually performed the action. In AI-heavy environments, the distinction between model output, agent action, and operator approval should be preserved in telemetry so investigators can reconstruct accountability.
Organisations also get this wrong when they treat deployment simplicity as the same thing as operational sufficiency. The right answer is usually a layered model, with clear coverage targets and review cycles, informed by the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework when AI systems are in scope.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
MITRE ATLAS, OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | DE.CM-1 | Telemetry is the core evidence source for continuous monitoring and detection coverage. |
| MITRE ATLAS | AI telemetry must reveal adversarial behavior across prompts, tools, and outputs. | |
| OWASP Agentic AI Top 10 | Agentic systems need traceability for tool use and action attribution. | |
| NIST AI RMF | GOVERN | Telemetry choices should follow governance, purpose limitation, and risk ownership. |
| CSA MAESTRO | MAESTRO helps model observability needs for agentic AI control planes and trust boundaries. |
Define telemetry policy, ownership, and review criteria under GOVERN before choosing collection methods.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 2, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
