Topic starter
25/10/2026 11:04 am
TL;DR: Agentic AI trust now has to be decided at execution time, not after the fact, according to Tumeryk. Delegation, policy enforcement and observability need to work together so authority does not outrun governance across agent chains.
Editorial analysis by NHI Mgmt Group, based on content published by Tumeryk: “Building Trusted Agentic AI with Embedded Guardrails, Trust Scores, and Continuous Red Teaming”.
Key questions
Q: Why do traditional AI controls fall short for agent-to-agent workflows?
A: Traditional AI controls usually focus on model output, content safety or later audit.
Q: What are the best practices for observability in agentic AI systems?
A: Capture the full decision trail, not just the final output.
Q: How should security teams govern agentic AI when agents can delegate to sub-agents?
A: Treat delegation as a first-class identity boundary.
Practitioner guidance
- Implement runtime trust evaluation Score every agent call at execution time and block or step up when the trust score indicates policy, data exposure or behaviour risk.
- Define explicit policy boundaries Express what agents may do, which tools they may invoke and which delegation paths are forbidden, then enforce those limits in the control layer.
- Record decision-level observability Capture the call, response, trust score and policy outcome together so security, compliance and incident teams can reconstruct agent behaviour.
Bottom line: Agentic AI changes the control boundary by turning trust into an execution-time decision instead of a retrospective review problem.
Glossary terms: Agentic AI, Trust Scoring, Delegated Authority Model, Observability For Agentic AI
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:17 pm
Runtime trust is becoming the new security boundary for agentic AI. Once agents can invoke tools and delegate work to other agents, a delayed review model no longer contains risk. Trust scoring at execution time is the more defensible pattern because the decision can be stopped before an unsafe action fans out across the workflow. Practitioners should treat the trust signal as part of the control plane, not as an analytics overlay.
A few things that frame the scale:
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Should IAM teams be involved in AI agent governance from the start?
A: Yes, because AI agents inherit access, secrets, and revocation problems that are already IAM concerns. IAM teams should define entitlement boundaries, session scope, audit expectations, and offboarding rules before the first production rollout. If those controls are deferred, the programme will scale faster than it can be governed.
👉 Read our full editorial: Agentic AI trust scoring and guardrails move into runtime control
This post was modified 7 days ago by NHI Mgmt Group