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25/06/2026 10:27 pm
TL;DR: Generative AI becomes reliable only when successful reasoning is converted into deterministic artifacts, with human oversight, success-path capture, and skill stores turning probabilistic outputs into repeatable workflows, according to Kong. That shift matters because access, auditability, and control must be designed into agentic systems before they are trusted in production.
NHIMG editorial — based on content published by Kong: Moving from Probabilistic Reasoning to Deterministic Execution
Questions worth separating out
Q: How should security teams govern reusable AI workflows in production?
A: Security teams should treat reusable AI workflows like privileged machine actions.
Q: Why do deterministic AI controls matter for IAM and NHI programmes?
A: Deterministic controls matter because prompts alone do not guarantee repeatable behaviour.
Q: What do teams get wrong about human-in-the-loop AI governance?
A: Teams often treat human review as a final quality check instead of a gate for reuse.
Practitioner guidance
- Map which AI actions must become deterministic Inventory recurring AI tasks and separate exploration from repeatable execution.
- Put human approval before artifact promotion Require a human reviewer to validate the first successful path, then decide whether it can enter a skill store or long-term memory.
- Apply lifecycle ownership to reusable AI artifacts Name owners, define review cadence, and set retirement rules for each executable AI artifact.
What's in the full article
Kong's full blog post covers the operational detail this analysis intentionally leaves for the source:
- The step-by-step pattern for capturing a success path and converting it into executable code.
- The practical role of skill stores in reusing validated AI workflows across incidents and tasks.
- The distinction between temporary model reasoning and long-term deterministic artifacts.
- The article's own examples of how artifact-driven execution reduces hallucinations and operational risk.
👉 Read Kong's analysis of deterministic execution for agentic AI →
Deterministic execution for agentic AI - what IAM teams need?
Explore further
25/06/2026 10:59 pm
Deterministic execution is an identity governance problem, not just an AI engineering problem. Once a generative system can call tools, write artifacts, and reuse prior success paths, the core question becomes who governs the action boundary. That boundary is familiar to IAM teams because it looks like privileged execution, only now the executor is probabilistic unless it is forced into deterministic control structures. The implication is that agentic AI cannot be treated as a pure application layer concern.
A few things that frame the scale:
- 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, according to The State of Non-Human Identity Security.
- The same research found that only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs.
A question worth separating out:
Q: How do organisations know if an AI skill store is safe to use?
A: An AI skill store is safe when every stored workflow has a named owner, validated input conditions, version control, and a retirement path. If entries are reused without those controls, the store becomes a hidden execution layer rather than a governed one, which undermines auditability and change management.
👉 Read our full editorial: Deterministic execution is the missing control layer for agentic AI
