Flagship study
AgentGuard
A product experience exploring how autonomous AI agents can be monitored, risk-assessed and governed through approvals, policy controls and audit-oriented workflows.
- Role
- Product concept, security direction and interface design
- Year
- 2026
- Status
- PROTOTYPE
- Technologies
- React, TypeScript, Agent monitoring, Policy workflows, Prompt-injection awareness, Approval controls
About this project
This record separates implemented work, demonstrated product flows and capabilities still in development.
Problem
As AI agents gain access to tools and business context, teams need to understand agent state, review risky actions and define where human approval is required.
Approach
AgentGuard presents a fleet view, agent health and risk signals, policy concepts, approval states, incident context and containment-oriented language in one command-centre experience.
Architecture
The experience is structured around agent identity, runtime signals, policy evaluation, approval boundaries and an audit trail. Aura's separate agent-registry foundation should not be presented as the same backend.
Challenges
The design must make agentic AI security understandable without implying that a visual state change proves live blocking, containment or prompt-injection prevention.
Outcomes
The supplied captures document a coherent AgentGuard product direction across the control-layer homepage, capabilities, audience framing, compliance positioning and simulated command-center demo.
Integrations
- Agent registry concepts
- Policy and approval model
- Audit-oriented workflows
- Runtime health signals
Project evidence
