Topic starter
18/08/2026 2:25 pm
TL;DR: Routing coding agents through an AI gateway captures tokens, cost, latency, and policy decisions, but it still misses reasoning, tool choices, and inbound context, according to Fiddler. Effective governance requires correlating gateway logs with agent-side telemetry before autonomy expands.
Editorial analysis by NHI Mgmt Group, based on content published by Fiddler: “Internal Coding Agent Monitoring: What the AI Gateway Sees and What It Misses”.
Key questions
Q: What breaks when coding agents are monitored only through an AI gateway?
A: Gateway-only monitoring captures traffic, tokens, latency, and policy decisions, but it misses the reasoning steps, tool choices, and inbound context that shaped the action.
Q: Why do coding agents need more than proxy logs for governance?
A: Proxy logs show transport, not intent.
Q: What are the signs that a coding agent security control is failing?
A: A control is failing when benign-looking inputs can still trigger command execution, when skills or tools remain usable after the original task ends, or when untrusted content can influence agent actions across workflow boundaries.
Practitioner guidance
- Implement correlated agent and gateway telemetry Propagate a shared trace or session identifier from the coding agent into gateway logs so each request can be reconstructed with its internal decision chain.
- Instrument agent-side spans and tool calls Capture reasoning steps, tool invocations, and retrieved context with agent-side telemetry so the gateway record can be explained rather than guessed.
- Limit payload logging by default Record metadata, policy outcomes, and correlation IDs first, then gate full prompt and response capture behind redaction, sampling, and retention rules.
Bottom line: Gateway telemetry shows what coding agents send and receive, but it does not explain why the agent chose a tool, a response, or a risky action.
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18/08/2026 3:15 pm
Gateway telemetry is a boundary control, not a governance control. It tells you what crossed the wire, but not what the agent understood, prioritised, or decided after receiving it. That distinction matters because coding agents behave through chains of tool calls and retrieved context, not single-shot prompts. Practitioners should treat proxy logs as necessary evidence, not sufficient oversight.
A few things that frame the scale:
- 7% of security leaders admit they do not know how often their AI systems are making autonomous changes to infrastructure, according to the 2026 Infrastructure Identity Survey.
- Claude Code-assisted commits leaked secrets at a rate of 3.2%, more than double the human-only baseline of 1.5%, with peaks reaching 31 secrets per 1,000 commits in August 2025, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: How should enterprises govern code agents without losing developer velocity?
A: Enterprises should treat code agents as runtime actors, not just code producers. The right model is unified visibility, centralized policy enforcement, and identity-backed control across all agent workflows. That means discovering shadow tools, restricting tool calls by context, monitoring for prompt injection, and blocking sensitive data movement before the agent can silently undermine security posture.
👉 Read our full editorial: Internal coding agent monitoring needs more than gateway telemetry