What a Runtime Control Dry Run Validates
A runtime control dry run is distinct from standard functional or QA testing. Functional testing confirms that an agent produces correct outputs. A runtime control dry run confirms that the agent's access to tools, data, and downstream systems is correctly scoped, enforced, and logged before it operates in production. This distinction matters because agentic systems can behave correctly at the task level while still exceeding intended authority at the permission level. Frameworks such as NIST's AI Risk Management Framework describe testing and validation as lifecycle functions that extend into pre-deployment, and OWASP's guidance on large language model applications identifies excessive agency and insecure tool or plugin design as risks that require deliberate verification rather than incidental discovery. A dry run operationalizes these principles into a defined pre-go-live checkpoint rather than treating them as background best practice.
Common Configuration Gaps Surfaced During Dry Runs
No verified dataset documents standardized failure rates for agent runtime dry runs, so any specific frequency claims should be treated with caution. However, established control frameworks point to recurring categories of misconfiguration that a dry run is designed to catch:
- Agents are sometimes issued broad, shared, or human-equivalent credentials rather than distinct machine identities, which undermines traceability.
- Tool permissions are frequently granted at the API or connector level rather than scoped to individual actions, which is the condition OWASP describes as excessive agency.
- Policy decision and enforcement logic is sometimes conflated into a single component, making it difficult to test denial paths in isolation.
- Audit logs may capture successful actions but omit denied attempts, which limits their usefulness for post-incident review.
A dry run is structured specifically to surface these categories of gaps before they become production incidents rather than after.
Structuring Sign-off Between Security, Platform Engineering, and Governance
A dry run should conclude with a defined sign-off process rather than an informal handoff. Security teams typically confirm that identity issuance, permission scoping, and policy enforcement meet least-privilege requirements. Platform engineering confirms that the logging pipeline used in the dry run matches the one intended for production, since testing against a different pipeline does not validate real audit behavior. Governance stakeholders confirm that dry run outcomes are recorded as part of the system's ongoing risk management documentation, consistent with the Manage function described in NIST's AI Risk Management Framework. Any policy exceptions identified during the dry run should have a named owner responsible for approval or remediation before go-live. Recording this sign-off explicitly reduces ambiguity about who approved which risk tradeoff and supports later compliance or audit review.