Use the same contract for both. Define approved instrumentation libraries, required spans, allowed metric labels, severity rules, forbidden data types, and correlation fields in repository-level guidance and CI checks. Then require agents and humans to produce telemetry that supports dashboards, runbooks, and incident review without relying on local judgment.
How to govern observability across coding agents and developers
The practical answer is to treat observability as a shared control surface, not as a special exception for agents. If both humans and coding agents can change code, deploy artifacts, or touch production paths, they should emit comparable telemetry under the same rules so review, detection, and incident response do not depend on who made the change.
The governance question is less about whether agents can be observed and more about whether teams can trust the resulting telemetry. That means setting one standard for instrumentation, field quality, and correlation, then enforcing it in the repo and CI so the evidence exists before an incident forces you to rely on it.
What the shared observability contract should define
Start with a contract that is specific enough to be enforced automatically. Define approved instrumentation libraries, required spans, allowed metric labels, severity rules, forbidden data types, and the correlation fields that must travel with every action. Put those rules close to the code so the same checks apply whether a commit comes from a human or an agent.
This approach keeps observability aligned with operations instead of leaving it as a best-effort habit. It also avoids a common failure mode where teams instrument agent activity richly but leave human activity inconsistent, which breaks comparisons in dashboards and weakens post-incident reconstruction.
For teams using OpenTelemetry-style patterns, the key governance decision is not which actor generated the event, but whether the event can be stitched into a usable trace, metric, or log record. That is why correlation identifiers, span naming, and a stable severity taxonomy matter more than ad hoc commentary in free-text fields. The OWASP Cheat Sheet Series is useful here because it reinforces disciplined implementation around logging, authentication, and safe handling of sensitive data in application telemetry.
How to make the same rules work for coding agents and humans
Apply one policy layer and one enforcement layer. Repository guidance should say what must be emitted, while CI should verify that the required instrumentation exists and that forbidden fields, such as secrets or high-risk personal data, are not being logged. This makes observability a release criterion, not a developer preference.
For coding agents, add one extra governance rule: the agent may generate telemetry, but it should not be allowed to invent its own observability schema. Agent-authored code should comply with the same span structure, metric naming, and correlation requirements as human-authored code, because mixed telemetry only works when the records are comparable. NHIMG’s AI Coding Agents Security Guide is directly relevant because it ties coding agents to secrets in context, sandboxing, and supply chain risk.
The other control boundary is data minimisation. Observability should support dashboards, runbooks, and incident review, but not create a second data-exposure channel. For that reason, teams should classify which labels and fields are allowed, which are redacted, and which are entirely forbidden. This is especially important when agents run with broader context than humans, because they can accidentally persist sensitive material into logs, traces, or comments at scale.
When teams need a concrete example of why this matters, agent compromise is often visible first in telemetry, not in source code. NHIMG’s AI Agent Observability, Audit and Incident Response Guide shows why action attribution, log quality, and tested response paths matter once an agent starts making autonomous changes. The governance lesson still applies to humans: if you cannot attribute an action cleanly, you cannot review it cleanly.
What good governance looks like in practice
Good governance produces telemetry that is boringly consistent. A reviewer should be able to ask, “What changed, who or what changed it, what was touched, what was the severity, and how do I trace the blast radius?” and get the same answer from both human and agent activity. If that answer depends on who authored the code, the observability model is too loose.
A strong operating model usually has three visible signs: instrumentation patterns are reusable, CI rejects missing or unsafe telemetry, and incident review can rely on the same fields across services and actor types. That is the standard to aim for, because it gives you a single operational language for routine delivery and exceptional response.
NHIMG’s AI Agent Authorisation Guide is useful as a companion reference because observability and authorisation fail together when teams cannot tell which action was permitted, which was executed, and which should have been blocked. The governance takeaway is that telemetry must support accountability, not merely volume.
Practitioner takeaway: Treat observability as a contract that both humans and coding agents must satisfy, then enforce it at review time and build time so attribution, alerting, and incident reconstruction are dependable before an incident occurs.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 addresses the attack and risk surface, while OWASP ASVS, NIST SP 800-53 Rev 5 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP ASVS | V16 — Security Logging and Error Handling | Shared telemetry governance depends on consistent logging and error handling. |
| Recommendation — Enforce secure, structured logging so human and agent actions remain attributable and reviewable. | ||
| NIST SP 800-53 Rev 5 | AU-2 — Event Logging | The question is about defining what observability events must be captured and reviewed. |
| AU-6 — Audit Review, Analysis, and Reporting | Telemetry must support dashboards, runbooks, and incident review across actor types. | |
| Recommendation — Define required audit events for both humans and coding agents. Review and correlate logs so agent and human actions can be investigated consistently. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Coding agents need observability that reveals when authority or action scope is abused. |
| Recommendation — Instrument agent actions so privilege abuse is visible in review and response. | ||
| CIS Controls v8 | CIS-8 — Audit Log Management | The topic centers on standardized telemetry collection, retention, and review. |
| Recommendation — Standardize log collection and retention for both agent and human activity. | ||