- Updated: April 1, 2026
- 2 min read
Claude Code AI Agents Get Real‑Time Dashboard for Team Monitoring – Boost Collaboration and Performance
Claude Code AI Agents Now Feature a Real‑Time Dashboard for Team Monitoring
The open‑source agents‑observe repository introduces a powerful real‑time observability layer for Claude Code agents. Designed for development teams that rely on AI‑driven workflows, the new dashboard gives instant insight into agent health, execution metrics, and collaborative performance.
Why Real‑Time Monitoring Matters
AI agents often run autonomously across multiple environments. Without visibility, teams can miss bottlenecks, errors, or unexpected behavior. The dashboard solves this by aggregating logs, execution traces, and resource usage into a single, easy‑to‑read interface.
Key Features
- Live Metrics: CPU, memory, and latency stats update every second.
- Agent Health Indicators: Visual cues for running, idle, or failed states.
- Team Collaboration View: See which agents are assigned to which team members and track progress in real time.
- Customizable Alerts: Configure thresholds for automatic notifications.
How to Install
1. Clone the repository: git clone https://github.com/simple10/agents-observe.git
2. Install dependencies with npm install.
3. Run the dashboard using npm start and point your Claude Code agents to the provided endpoint.
Integration with UBOS Solutions
UBOS already offers complementary tools for AI development. Pair the real‑time dashboard with our AI agents platform and the dashboard tools suite to create a seamless end‑to‑end workflow.
Looking Ahead
The project roadmap includes advanced analytics, multi‑tenant support, and deeper integration with Claude Code’s native APIs. Contributions are welcome via the GitHub repository.
Stay ahead of the curve by adopting this observability layer and give your AI‑agent teams the visibility they need to innovate faster.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.