The Command Center for Your AI Agents
See what every agent is doing, own every task, and get more done per dollar — across every machine and every model.
Then own every task, finish it in fewer turns, and know what it really cost.
Every session on every machine: which agent, which repo, which tool it's calling.
Spot dead runs, retry loops and wasted turns, then see whether each task is getting cheaper to finish.
Tie agent work to the ticket or PR it served, the person who launched it, and what it spent.
| Task | Owner | Agent | Cost |
|---|---|---|---|
| PR #412 billing retry | ana | claude-code | $2.71 |
| JIRA-88 flaky test | sam | codex-cli | $6.02 |
| PR #415 docs sweep | ana | cursor | $0.48 |
| (no task linked) | — | gemini-cli | $3.10 |
Tokens aren't equal across Claude, GPT and Gemini. Riva puts them on one scale: cost per completed task.
Fewest tokens ≠ cheapest task. Try it with your numbers →
Snippets show sample data.
Everything Riva does on one machine, across your whole organization.
Every machine, every agent, one view. Live status, geographic distribution, and per-client drill-down for your entire deployment.
Organizations, teams, and devices with scoped API keys. Every client pairs with a per-tenant credential — no shared secrets.
HMAC-SHA256 hash-chained audit log ingested from every machine. Verifiable end to end — an entry can't be altered without breaking the chain.
Agent lifecycle events stream over SSE the moment they happen — agent started, agent stopped, policy violated, anomaly detected.
Define boundary policies centrally — file, network, and process limits — and push them to every client on heartbeat.
Route events to Slack, PagerDuty, or any endpoint. CEF export feeds Splunk and QRadar so agent activity lands in your existing SOC.
Session timelines, dead-end and retry-loop detection, and efficiency trends — aggregated across machines, not just one laptop.
Cost per agent, per model, per team, per day. Know exactly where your token spend goes before the invoice arrives.
Drop-in observability for the agents your teams already run.
Stability and security don't come from hoping agents behave. They come from watching them.
Install the agent on each machine, link it to your Riva Server, and the fleet reports in.
# Install Riva on a machine $ curl -fsSL https://rivalabs.ai/install.sh | sh # Link it to your organization's Riva Server $ riva link start https://rivalabs.your-company.ai # Approve the device in the Command Center — done. # Agents, audit entries, and policies now sync automatically. # Watch the fleet from your terminal, or open the dashboard $ riva watch
A real-time view of every machine reporting to this Riva Server. Link one with riva link start.