What was the issue? While Hive provides a powerful backend for autonomous agents, execution was a "black box." Debugging complex agent workflows—like when a scraper hits a 403 and needs to rotate proxies—was painful through terminal logs. There was no easy way to visualize the decision-making process, track token usage, or monitor costs in real-time.

How did you fix it? I built the Web Dashboard (hive/web) using Next.js and Tailwind CSS to provide a dedicated observability layer that syncs directly with the local runtime.

Real-time Visualization: Created a live view of agent runs, showing every step, tool call, and state change as it happens. Decision Tracing: Implemented a timeline view that breaks down exactly why an agent made a decision (e.g., "Switching to residential proxy due to 403 error") and what options it discarded. Performance Metrics: Added effortless tracking for token consumption, latency, and cost per run. Why does it matter? Trust is the biggest barrier to adopting AI agents in production. By highlighting "Self-Healing" events and making the agent's "brain" visible, we move from "magic" to engineering. This dashboard gives developers the confidence to deploy agents and the insights needed to optimize them when they fail.