Dashboards that think

Every chart has an agent

When a chart is created, a dedicated monitoring agent is assigned to it. This agent watches the data stream continuously -- detecting anomalies, identifying trends, and predicting future values. When something unusual happens, it doesn't just flag a number. It explains why.

Four specialized swarms power every dashboard: Monitor (real-time anomaly detection), Enricher (contextual data augmentation), Predictor (trend forecasting), and Alerter (threshold-based notifications).

Talk to your dashboards

The Cognitive Command Bar is the primary interface to your entire agent system. Type a question about any chart, any metric, any trend. The system classifies your intent, routes to the right specialist agent, and streams a response — with every routing decision, delegation, and model selection visible in a Glass Box DAG trace.

@mention specific agents for direct access. Mention two or more for a group chat where agents collaborate on your question. The command bar adapts — general chat, dashboard generation, agent building, and coding flows all route through the same interface.

Four layers of memory

Most AI platforms have no memory at all. Close the tab, start over. DeepHarness implements a four-layer memory architecture inspired by cognitive science. Working memory holds the active conversation. Episodic memory records past sessions and outcomes. Semantic memory stores rules, facts, and preferences across your organization. Procedural memory captures learned skills and workflows.

After every session, a consolidation agent reviews the entire exchange and distills reusable knowledge — rules you stated, lessons learned, facts about your domain. These become structured memories with confidence scores, recalled deterministically in every future conversation for every team member.

Say "always use UTC timestamps" once. It becomes an organization-wide rule. Tell the system your timezone. It remembers. Every fact, rule, and profile detail can be viewed, edited, or deleted through the Memory Inspector — full transparency into what the AI knows and why.

Verified data, not plausible data

When data isn't reachable, most AI tools don't stop — they fill the gap with numbers that look right. On a dashboard, a hallucination doesn't look like a hallucination. It looks like a number. And people act on numbers.

Every widget DeepHarness builds passes an adversarial data-integrity check before it renders. Three independent lenses inspect each binding: does the field actually exist in the live response, is the value the right type to plot, and did it come from your real source on this run — not a fallback.

If a widget can't prove where its numbers came from, the build fails loudly instead of rendering a confident-looking lie. The verification runs in the open, on the timeline, so you see exactly which check passed and which didn't. A failed check is information. A fabricated number pretending to be real is not.