From Passive Observability to Active Enforcement for AI Agents
Most production AI-agent discussions stop at observability. Traces are useful, but traces only explain the incident after the agent has already run the tool call.
Tool-using agents (Claude Code, Codex, Cursor, n8n, OpenClaw-style runners) fail differently than chatbots. They fail by doing work: retries, shell hops, cloud escalations, infinite loops that burn budget overnight.
Governance without a runtime hard stop is theater.
What actually matters before the hop:
- budgets that fail closed
- tool permissioning before the action
- receipts that policy actually fired
That is ThumbGate: an infrastructure firewall for AI coding agents. Pre-action gates, feedback capture, prevention rules.
Start here
If one unattended session burned a week of tokens on a repeated failure class, that is what we scope in a diagnostic:
$499 Agent Reliability Diagnostic:
https://thumbgate.ai/diagnostic?utm_source=devto&utm_medium=social&utm_campaign=agent_reliability_diagnostic&cta_id=agent_reliability_gapfill_20260723_devto_diag
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