Agent Studio: Where AI Agents Get Built.

One platform to build and deploy operational agents. Grounded in your skills, your systems, and your data.

Transform your Data into a Working Agent.

Build and test your own agent. Choose the capabilities it needs, teach it with real examples, simulate it, then deploy it – no engineering required.

Define agent skills

Skills are the capabilities your agent can perform – inspect, guide, verify, escalate. Define each skill against real procedures and data so the agent does specific work, not open-ended chat.

Details

  • Skill library for operational tasks
  • Map skills to SOPs and annotated procedures
  • Inputs, outputs, and success criteria per skill
  • Confidence thresholds and human handoff rules
  • Reuse skills across agents, sites, and roles

Connect tools and systems

Wire the agent into the systems where work already happens. APIs, machines, sensors, wearables, ticketing, and enterprise apps – so guidance and actions stay inside your stack. 

Details

  • API and MCP-ready integrations
  • Cameras, wearables, edge devices, and robots
  • Enterprise systems (CMMS, MES, ITSM, ERP)
  • Live sensor and machine state context
  • Authorizations, permissions, and audit logging

Build the agent

Compose agents visually or in code. Combine skills, models, knowledge, and tools into a controlled agent that understands your environment and follows your rules. 

Details

  • Visual builder and templates
  • Code and API extensibility
  • Attach models from the model registry
  • Ground answers in your knowledge base and Physical AI context
  • Policies, guardrails, and escalation paths
  • Multimodal responses (text, video, visuals)

Simulate and validate

Test agents on real operational data before rollout. Measure accuracy, safety, and task success. Fix gaps in skills, data, or models before anyone depends on the agent on the floor. 

Details

  • Replay real scenarios and edge cases
  • Task success, intervention rate, and accuracy metrics
  • Safety and policy checks
  • Human review of uncertain paths
  • Go / no-go gates per skill and site

Deploy with control

Roll out to operators, cameras, wearables, edge, or robots – on Ramblr infrastructure or yours. Control who gets which agent, where it runs, and how it escalates.

Details

  • Deploy to workforce, edge, cloud, or robot runtime
  • Customer infra or Ramblr-managed hosting
  • Role-level, site-level, and line-level rollout
  • Version pinning and staged release
  • Instant rollback

Monitor and improve

Every execution becomes structured feedback. Watch performance live, catch drift, and feed outcomes back into the Data Engine so the next dataset, model, and agent version is better. 

Details

  • Live monitoring of agent runs
  • Deviation and failure capture
  • Operator feedback into training data
  • Continuous eval against production traffic
  • Closed loop: deploy → learn → retrain → redeploy