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21/08/2026 4:50 pm
TL;DR: A new workflow can let teams instrument AI applications, resolve OpenTelemetry dependency conflicts, and verify traces in the IDE without manual code changes, according to Arize. The practical shift is not just faster setup, but a more governed path to observing agent behaviour, where traceability becomes part of the development workflow rather than an afterthought.
NHIMG editorial — based on content published by Arize: Google Antigravity and Arize AX’s MCP Tracing Assistant
Questions worth separating out
Q: How should security teams govern AI agents that can choose tools at runtime?
A: Security teams should govern runtime agent choice as an access event, not as a simple application action.
Q: Why do AI agents complicate traditional observability and access control models?
A: AI agents can act across tools, data, and code paths in ways that are hard to predict from static role definitions.
Q: What do security teams get wrong about AI traceability?
A: They often treat traceability as reporting instead of control.
Practitioner guidance
- Define traceability requirements for every production agent Require trace capture for tool calls, package changes, and external data access before an agent is approved for production use.
- Govern instrumentation dependencies as production assets Track OpenTelemetry libraries, instrumentors, and agent SDKs in the same change-control process as application code.
- Pair observability with least-privilege for agents Do not use tracing as a substitute for access control.
What's in the full article
Arize's full article covers the implementation detail this post intentionally leaves at the governance layer:
- The exact Antigravity workflow used to add the Arize AX MCP server and trigger instrumentation from inside the IDE.
- The dependency-resolution steps for OpenTelemetry conflicts, including package upgrades and compatibility fixes.
- The final verification walkthrough used to confirm traces were flowing to Arize AX.
- The specific artifacts generated during the process, including plans, task notes, and debug scripts.
👉 Read Arize's analysis of MCP tracing for AI agents in Antigravity →
AI agent tracing in Antigravity: what changes for observability teams?
Explore further
21/08/2026 5:08 pm
Tracing is becoming a governance control for agentic systems, not just an engineering aid. Once AI agents can call tools and mutate workflows, telemetry becomes evidence of delegation, scope, and runtime behaviour. That makes trace data relevant to IAM, PAM, and NHI governance because it helps answer who or what acted, under what permissions, and with what effect. Practitioners should treat observability as part of the identity control stack, not a separate developer utility.
A question worth separating out:
Q: How should organisations decide whether agent instrumentation is enough for governance?
A: Instrumentation is enough only when it is paired with access limits, approval workflows, and dependency governance. If the agent can reach sensitive systems, modify packages, or operate with broad secrets, then traces are evidence, not control. Governance is working when the organisation can both observe and constrain the agent’s runtime behaviour.
👉 Read our full editorial: Arize AX MCP tracing in Antigravity shifts agent observability
