In security operations, no two investigations are the same – and neither are the visualizations they require. Each procedure run generates a unique visualization problem, shaped by the data, the context, and the unfolding investigation.
Traditionally, a data scientist or analyst would need to review the specific results, extract what matters for that run, and manually organize the information into visualizations that tell a compelling, coherent story.
With the New Agent Debrief, that process is now automated and orchestrated by a team of specialized AI Agents. Here’s how it works:
- Debrief Coordinator Agent reviews all outputs from the procedure’s tasks.
- It creates a visualization plan, identifying what should be shown, how it should be organized, and the priority of each element.
- The Coordinator then assigns tasks to multiple specialized visualization agents, each expert in rendering a particular type of data – whether it’s a timeline, a table, key-value pairs, or code snippets.
- These agents work together and execute their parts in sequence, assembling the Debrief as an ordered set of “cards” that match the investigation’s priorities and flow.
| Bricklayer Data Visualization Agent Team | |||||
| Debrief Coordinator Agent | Timeline Specialist Agent | Table Specialist Agent | Key Value Pair Specialist Agent | Code Snippet Agent | Chart Specialist Agent |
Expanded Visualization Toolkit
The Agent Debrief now supports:
- Code Snippet
- Table
- Timeline
- Key-Value Pairs
- Rich Text Field
- Line Chart
- Bar Chart
- Pie Chart
These building blocks can be combined in dynamic ways, producing a tailored user experience that evolves with the investigation. Whether summarizing a simple alert triage or unpacking a complex, multi-stage incident, the Debrief presents exactly what’s needed – no more, no less.
Why Your SOC Needs a Multi Agent System for Data Visualization
This approach goes beyond a static, one-size-fits-all summary. It creates a live, adaptive interface that mirrors the way analysts think, helping them stay focused on decision-making rather than data wrangling.
By letting AI handle the organization, prioritization, and presentation of investigative outputs, analysts gain time, clarity, and confidence in every procedure run.
