For companies deploying AI agents in consequential contexts — healthcare, financial services, enterprise software — we design the accountability structures their agents need to be trusted, deployed, and defended.
Schedule a ConversationAgents now approve payment plans, screen candidates, route patients, and execute transactions on behalf of real people — at scale and at speed. The decisions are consequential. The accountability is not yet defined.
Compliance frameworks were built for systems, not agents. Internal responsible-AI programs cannot independently validate themselves. Law firms produce documents, not operational structures. Multi-party agent chains make accountability ambiguous by default.
The gap is not awareness. It is architecture.
Each engagement produces operational governance — structures your team can use, documentation enterprise buyers and regulators can evaluate, and a posture that holds up under scrutiny.
A focused four- to six-week engagement that maps where your AI agents make or influence decisions affecting real people, identifies the accountability gaps, and produces a Governance Posture Document covering decision boundaries, escalation, accountability, and audit framework.
A two- to three-month build-out that designs the full accountability architecture for your agent system — decision boundaries, escalation protocols, accountability mapping across each layer of the stack, and audit-ready documentation that procurement and legal teams can evaluate.
For companies that want governance to remain current as agents evolve. Standing advisory on new decision points, regulatory developments, and material changes in agent behavior — without rebuilding the structure each time the technology shifts.
Everyone else governs AI at the policy or platform level. We govern AI at the point of impact.
An illustrative excerpt from a Governance Posture Document for a healthcare billing agent. Each engagement produces this for the specific decisions your agents are making.
| Decision Point | Boundary | Escalation | Accountability | Audit |
|---|---|---|---|---|
| Offer a payment plan | Autonomous | If financial hardship is indicated, escalate to a human financial-assistance reviewer. | Health system owns configuration; platform owns the constraint enforcement. | Plan offered, patient response, hardship signals detected, escalation triggered. |
| Recommend waiving dispute rights | Never automated | Immediate supervisor review required; patient is informed of their rights before any action. | Health system legal owns the review; platform is prohibited from enabling the action. | Full transcript, written consent, supervisor sign-off, notification record. |
| Screen Medicaid eligibility | Autonomous screening | Enrollment requires human action; patient is informed of eligibility before any payment plan is finalized. | Platform owns screening accuracy; health system owns enrollment. | Screening result, patient notification, enrollment status, timing of each step. |
Illustrative only. Each engagement maps your specific decision points, boundaries, and escalation paths.
Procurement, legal, and security teams are asking governance questions vendors cannot yet answer. A validated governance posture turns a months-long deal blocker into a documented, defensible response.
The EU AI Act is in force. U.S. federal and state frameworks are arriving. Companies with operational governance already in place are positioned to shape the standard rather than scramble to retrofit it.
Agent failures do not fail one user at a time — they fail every user simultaneously. Decision boundaries and escalation protocols reduce the surface area of the failures that scale.
Validated, third-party governance is concrete in a market crowded with aspirational AI ethics statements. It is the difference between a principle and a procurement-ready posture.
In neuroscience, glia are the cells that hold the nervous system together. They don't make the decisions — neurons do.
Glia provide the structure, protection, and boundaries that ensure the system functions.
That is what governance does for AI agent systems.
GliaNet Advisory is led by two principals with complementary backgrounds in technology policy, governance design, and commercial execution.
More than twelve years at Google leading technology policy and platform governance. Developed the fiduciary governance frameworks that underpin GliaNet's methodology. Long-standing focus on accountability architectures for systems that act on behalf of people.
Background spanning quality-assurance process engineering, compliance standards design and implementation, go-to-market strategy, and AI governance operations. Leads engagement scoping, methodology application, and client delivery.
Guided by an independent Strategic Advisory Council comprising senior leaders in technology governance, institutional design, and public policy.
Institutional program funding from Omidyar Network, supporting development and validation of the underlying governance methodology.
A first conversation is a focused thirty minutes. We map what your agents are deciding, where the accountability gaps are, and whether an engagement is the right next step. No deck. No pitch. A working conversation.
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