Kaidera OS

Run AI worker teams

Understand leads, orchestrators, AI workers, deterministic jobs, dispatch, handoffs, approval, and run-state evidence.

Public docs for dev reviewLast verified 2026-07-01

Consolidated from Kaidera OS architecture notes, in-app help, and AI worker operating model docs.

Worker model

Kaidera OS treats workers as named roles inside a project. A worker has identity, role, harness, model route, reasoning setting, capabilities, and autonomy settings. Workers read and write through Cortex so the team shares context.

Interactive leads

The interactive lead is the operator-facing worker. Use it to clarify goals, convert intent into work, inspect evidence, and decide when a job is ready to hand off or approve.

Orchestrators

An orchestrator coordinates the work loop. It can read open work, prepare handoffs, route tasks to the right worker, watch run state, and surface decisions back for review. Orchestration should remain approval-aware.

AI workers

AI workers execute scoped tasks. They can be configured for functions such as engineering, documentation, design, QA, operations, analysis, or support. Each worker should have a narrow enough role that routing decisions are obvious.

Deterministic workers

Not every worker has to be model-backed. Deterministic workers can run fixed jobs such as checks, packaging tasks, indexing, report generation, or repeatable operations. Use them where a normal program is more reliable than a model.

Dispatch and handoffs

Work enters the system as a goal or handoff. The console can propose a worker, queue the job for approval, start a run, and stream state while the worker operates. Handoffs should contain scope, expected output, constraints, and review criteria.

Evidence and review

A run is not complete just because a worker stopped. Operators should review run state, changed files, test output, decisions, and any Cortex evidence before accepting work or enabling the next level of autonomy.

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