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21/08/2026 4:55 pm
TL;DR: TrueFoundry’s analysis says the line between tools, skills, and sub-agents is the real governance boundary in AI systems, because each rung changes who acts, what is auditable, and how much blast radius a delegate can create. Treating a reasoning delegate like a function call turns routine handoffs into hidden principal-level risk, according to TruFoundry.
NHIMG editorial — based on content published by TruFoundry: Tool vs. Skill vs. Sub-agent: The Delegation Spectrum and Its Governance
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should teams govern AI agent skills in production?
A: Treat skills as controlled runtime assets, not informal text.
Q: When does a sub-agent become a governance risk instead of a convenience?
A: A sub-agent becomes a governance risk when it reasons independently, uses tools on its own, and returns an opaque result that the parent cannot fully inspect.
Q: What do security teams get wrong about delegation in AI agent workflows?
A: They often assume a delegation chain is just a workflow detail.
Practitioner guidance
- Classify delegation before you govern it Inventory every AI capability as a tool, skill, or sub-agent, then map each class to the correct control surface.
- Assign principal-level controls to sub-agents Give each sub-agent its own identity, bounded permissions, resource budget, and trace.
- Version and review skills as governed artifacts Store reusable procedures in a registry with provenance, change control, and role-based access.
What's in the full article
TruFoundry's full blog post covers the operational detail this post intentionally leaves for the source:
- Step-by-step governance mapping for tools, skills, and sub-agents across the gateway, registry, and harness layers.
- Implementation examples for per-call guardrails, versioned skill artifacts, and isolated sub-agent traces.
- The delegated-workflow patterns that show how context windows, scope, and reviewability change as you move up the spectrum.
- Practical product-oriented explanation of how the MCP Gateway, Skills Registry, and Agent Harness separate control surfaces.
👉 Read TruFoundry's analysis of the delegation spectrum for AI gateways →
Tools, skills, and sub-agents: where governance breaks down?
Explore further
21/08/2026 5:12 pm
Delegation spectrum governance is an identity problem, not a taxonomy problem. The article correctly shows that tools, skills, and sub-agents represent different kinds of runtime delegation, not just different labels. Once that is true, the governance question changes from "what feature is this?" to "what identity surface does this create?" That is the right model for AI gateways and NHI governance alike, because control strength should match the delegate’s decision authority.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials, according to AI Agents: The New Attack Surface report.
- A separate finding shows that only 52% of companies can track and audit the data their AI agents access, which leaves 48% with no reliable compliance or investigation view.
A question worth separating out:
Q: How do you know if an AI gateway is governing delegation correctly?
A: You know it is working when the governance object matches the runtime behaviour. If a function call is logged, a procedure is versioned, and a delegate is traced as its own principal, the control model is aligned. If not, the system is probably misclassifying risk.
👉 Read our full editorial: Delegation spectrum in AI gateways: tools, skills, sub-agents
