Agents
Use AI and endpoint agents with evidence, policy, and control.
Agents help Reveals investigate signals, summarize evidence, inspect local posture, recommend next steps, and trigger approved actions. Hiddendata.ai keeps agent activity tied to source evidence, approvals, auditability, and measurable outcomes.
Agent Types
Hiddendata.ai presents agents as controlled contributors inside a Reveal, not as a separate place where users have to work. Agent activity should always connect back to the signal, evidence, decision, action, proof, and outcome the Reveal is managing.
- AI agents interpret evidence, summarize findings, draft artifacts, recommend next steps, and support approved workflows.
- Endpoint agents inspect local posture, runtime context, tool exposure, credential hygiene, and other device-side evidence.
- Reveal triggers decide when an agent-backed motion starts from a source event, webhook, schedule, or operator action.
- Operating Intelligence measures what the agent-supported motion detected, recommended, completed, and proved.
AI Agent Risk
AI Agent Risk Reveals help teams understand where AI tools and agentic systems may create unmanaged exposure. A scan can inventory local and connected agent surfaces, inspect MCP server posture, identify credential and secret hygiene issues, review model provider exposure, and flag risky tool execution paths.
- Agent and AI tool inventory
- MCP data source and server risk posture
- Hardcoded secret exposure and credential source hygiene
- Local and remote model provider exposure
- Tool execution privilege and approval posture
- Profile-scoped attribution and privacy-aware evidence handling
Status And Activity
Agent-backed Reveals should show whether the agent is available, what triggered the run, which customer, account, profile, or endpoint was inspected, what evidence was collected, and which actions still need ownership. Failed, stale, or blocked runs should be visible in Operating Intelligence so teams know where follow-up is stuck.
Governance
Agent activity should follow the same governance model as other Reveals. Use connection policies, actor mapping, approval gates, dedupe controls, rate limits, evidence retention, and source-system writeback rules to control what agents can do and how results are shared.
Show the inventory, risk findings, source evidence, recommended action, and proof trail for one completed scan.