Topic starter
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.
Editorial analysis by NHI Mgmt Group, based on content published by Arize: “Google Antigravity and Arize AX’s MCP Tracing Assistant: How to Trace Your Agent Without Writing Any Code”.
Key questions
Q: How should teams implement observability for AI agents that call tools and APIs?
A: Teams should instrument the agent path end to end, from user input through retrieval, tool execution, and final response.
Q: Why do tracing dependencies create operational risk in AI agent stacks?
A: Tracing often depends on a chain of compatible packages across OpenTelemetry, framework adapters, and the agent runtime.
Q: What are the signs that AI observability is being treated as a one-off task?
A: If instrumentation is added manually, undocumented, and difficult to reproduce after the first successful run, observability is being handled as a one-off task.
Practitioner guidance
- Instrument agent tracing through controlled workflows Standardise how developers add tracing to AI applications so instrumentation happens through approved workflows rather than ad hoc edits in each project.
- Pin tracing dependencies and adapters Track OpenTelemetry packages, framework-specific adapters, and AI runtime libraries together so version drift does not break trace collection during upgrades.
- Require verification artifacts for observability changes Store implementation plans, walkthroughs, and debug scripts with the code change so teams can review how tracing was added and confirmed.
Bottom line: AI observability is shifting from a post-deployment task to a development-time control, which changes how teams govern traceability and debugging.
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21/08/2026 5:08 pm
MCP-assisted observability is becoming part of agent governance, not just debugging. When tracing instructions are delivered through the same development workflow that builds the agent, observability moves upstream into the control plane for AI behaviour. That reduces the chance that teams ship opaque agent logic and only discover gaps after deployment. The broader governance point is that runtime visibility now has to be designed into the agent lifecycle, not retrofitted later.
A few things that frame the scale:
- 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: What should security teams review when an MCP server can modify agent code?
A: Teams should review the assistant’s access scope, the files it can read, and the configuration paths it can change. If the tool can alter source, dependencies, or runtime settings, it needs the same governance discipline applied to any code-changing automation, including ownership, approval boundaries, and change traceability.
👉 Read our full editorial: Arize AX MCP tracing in Antigravity shifts agent observability