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01/08/2026 10:50 pm
TL;DR: Agentic workflows that rely only on explicit rules fail when context lives in human judgment, not in logs or policy docs, according to Tonic. The practical problem is not whether AI can act, but when it should stop and escalate before a correct decision becomes an operational loss.
NHIMG editorial — based on content published by Tonic: Tacit Knowledge, Why Human Judgment is a Design Element, Not a Bug
Questions worth separating out
Q: How should security teams design escalation for AI agents that hit conflicting instructions?
A: Security teams should define pre-approved stop conditions for conflicts that the agent cannot resolve safely on its own.
Q: Why do AI agents need human judgment in high-stakes workflows?
A: AI agents can follow explicit rules, but many important decisions depend on tacit knowledge that was never written down.
Q: What breaks when agentic workflows rely only on written policy?
A: They become vulnerable to correct but disastrous decisions.
Practitioner guidance
- Define escalation stop conditions Document the exact combinations of conflicting signals that require human review, especially where policy compliance and operational safety point in different directions.
- Capture decision context at handoff Preserve the policies, signals, and local exceptions that triggered escalation so reviewers can see why the agent stopped and what it could not resolve.
- Separate routine automation from exception routing Allow the agent to execute low-risk tasks directly, but route high-impact conflicts into a bounded review path with clear ownership and auditability.
What's in the full article
Tonic's full article covers the conceptual detail this post intentionally leaves for the source:
- The article’s full explanation of tacit knowledge and the Polanyi Paradox in agentic systems.
- The collision model used to decide when an agent should stop and escalate.
- The Tier-0 patching scenario showing how rule-only automation can create business loss.
- The author’s framing of human judgment as a design element in autonomous workflows.
👉 Read Tonic's analysis of tacit knowledge and AI agent design →
Tacit knowledge and AI agents: where should human judgment sit?
Explore further
01/08/2026 10:58 pm
Tacit knowledge is now an AI governance requirement, not a soft skill. Agentic systems fail when organisations assume all important decisions can be translated into policy language. The article correctly identifies that expert judgment often lives outside manuals, logs, and prompts. That creates a governance gap in which the system can be compliant with instructions and still unsafe in context. Practitioners should treat tacit knowledge as part of the operating model, not as an informal exception path.
A question worth separating out:
Q: Who is accountable when an AI agent makes a risky decision?
A: Accountability should rest with the organisation that authorised the agent, the human owner of the workflow, and the control process that allowed the behaviour. If an agent can act independently, the programme must preserve attribution, action logs, and policy decisions so audit and remediation are possible after the event.
👉 Read our full editorial: Tacit knowledge is now a control plane issue for AI agents
