Explore/observability/One Monitor Does Not Fit All: Formalism-Agnostic Runtime Verification for Agentic AI
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Angelo Ferrando/One Monitor Does Not Fit All: Formalism-Agnostic Runtime Verification for Agentic AIUnknown

Agentic AI systems combine model decisions, mutable state, tool calls, and delegation to external agents. Monitoring them requires observations from different execution boundaries, while prevention requires control before an operation produces its effect. We present AgenticRV, a runtime-verification architecture that separates these boundaries from the monitoring formalism. Adapters emit canonical events; each monitor receives its own projection and abstraction; and a decision policy uses normalised verdicts to select a reaction supported by the responsible adapter. We formalise when observations suffice, monitor replacement preserves decisions, and blocking prevents unsafe effects. We also distinguish rejected attempts from completed effects, avoiding the assumption that a blocked request must leave every monitor in a permanent violation state. The Python implementation supports LangGraph, MCP, and A2A. A controlled LangGraph experiment combines an external Reelay temporal monitor with a programmatic report monitor; ablations show why both are needed for the selected requirements. Differential checks and cross-adapter tests provide further integration evidence. An LLM-driven incident-response example illustrates the same enforcement path, while existing microbenchmarks isolate framework costs. The results support reuse of monitoring methods at explicit control boundaries, subject to the stated observation and deployment assumptions.

observability
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Practical assessment
Should you use it?

✓ Best for

  • Agent tracing in production
  • LLM cost tracking
  • Debugging agent failures

◎ Strengths

  • Integrates with major frameworks
  • OpenTelemetry compatible

✕ Not ideal for

  • Small prototype projects
  • Teams without existing observability culture

⚠ Watch-outs

  • Data retention costs at scale
  • Privacy implications of storing LLM traces
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AgentHub Score

55
Score 55/100
Below average

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F
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