AIOS AI interviews (voice & chat) your experts and watches them work, converting tribal knowledge, undocumented processes, and SOPs into structured workflow primitives.

• AI Interviewer runs structured elicitation conversations and authors workflows live.
• AIOS Observer watches a desktop, converses via voice, and documents undocumented processes.
• Visual Workflow Studio: drag-and-drop graph editor with 10 node types and live validation.

AIOS AI extracts SME tacit knowledge.

The cognitive core converts captured processes into intent-based, AI-native workflows. Steps describe what to do; the runtime resolves how at execution time.

• Workflow Templates seed graphs with mapped fields and data pipelines.
• Conditional branching with replan-on-rerun: three-way verdicts (approved / rerun / rejected) feed back into the plan.
• Knowledge Sets and prompts attach to nodes, so context flows into every step.

Scripted workflows lock you in. AIOS resolves how at runtime.

Plan-before-execute, policy-gated tool calls, and human-in-the-loop approval at every action. Safety is the substrate, not a feature.

• Policy engine with per-tenant overrides controlling every tool call.
• Circuit breakers, kill switches, and human-in-the-loop gates at any node.
• Eval Gates: workflows can't promote Draft → Staging → Production without passing trajectory tests, custom LLM evaluators, and regression suites.
• Per-tenant LLM guardrails and an immutable audit ledger that survives cascade deletes.

Most platforms claim guardrails. AIOS proves it with the immutable audit ledger.

Adaptive execution against your real systems, with every action audited and scoped to a revocable identity.

• Connector ecosystem: REST, DB, terminal, email, Jira, Slack, Google Workspace, LinkedIn, webhooks; encrypted credential vault with auto OAuth refresh.
• Scheduling, webhook gates, and a documented REST API drive runs from any system.
• Multi-tenant isolation, sandbox environments, and MFA + SAML/OIDC + RBAC across 19+ resource types.

Enterprise reliability is a phrase. AIOS proves it with the sandbox and the audit ledger.

See every step the platform takes, from the overall workflow down to each individual decision and tool call. Every action is timed, costed, and replayable.

• 5-level execution traces (pipeline → agent → LLM → tool → walker node) over WebSocket streams plus OTLP telemetry to Grafana Cloud (Tempo / Loki / Prometheus).
• Every input, output, and decision logged with full lineage.
• Spend tracking: token-level cost ledger per execution, workflow, and tenant.
• Operator Dashboard: daily loop view of approvals, alerts, and run health.

Observability isn't a phrase. AIOS ships the spend ledger, the OTLP pipe, and the dashboard.

Every execution feeds layered memory and a self-improving context playbook. The platform doesn't just run workflows; it remembers them.

• Layered memory: episodic, semantic-graph, and procedural memory that persists across runs.
• Knowledge RAG: upload, chunk, embed, then attach Knowledge Sets to specific nodes for context-aware execution.
• Three-way verdicts (approved / rerun / rejected) feed plans, evals, and memory simultaneously, so humans, evaluators, and outcomes all write back to the core.
• Evolving Context Playbook: self-improving library with auto-creation, dedup, pruning, and preview.

Most platforms forget between runs. AIOS gets smarter every run.