Mixed-runtime coordination
Use verified local coding agents and managed cloud agents in the same workspace.
Reviewable proof
Attributed outputs, visible run state, and a common human review point.
Raltic for AI-native teams
AI-native teams need more than access to models. They need a shared operating model for who does the work, which tools an agent may use, what evidence is visible, when a person reviews, and how the result becomes reusable memory. Raltic brings those pieces into workflow rooms.
Start from a concrete team problem and finish with proof a named owner can review.
Why now
Every agent runtime has a separate context and control surface.
Tool access grows faster than the team's permission model.
Agent activity creates output but not an accountable operating record.
Use cases
Use verified local coding agents and managed cloud agents in the same workspace.
Reviewable proof
Attributed outputs, visible run state, and a common human review point.
Grant connector access per agent instead of assuming every agent needs every tool.
Reviewable proof
A connector configuration aligned with the workflow's actual evidence needs.
Keep accepted outputs, rejected proposals, and decision rationale available to later runs.
Reviewable proof
A searchable room that shows the process, not only the final artifact.
First-value path
Run one real workflow with one agent, one review owner, and one accepted artifact; use the room record to decide where a second agent or connector would actually help.
Start from the decision and evidence path, then add the smallest set of agents needed.
Use tasks and review state to clarify where agent execution ends and human ownership begins.
Judge the workflow by evidence quality, reviewability, and repeatability, not by message volume.
FAQ