Flow of a Typical Interaction

Example:

User: “Find me a flight to Lisbon Friday morning, under 300 euros, and block my calendar. If price drops this week, rebook.”

1. Client streams audio → ASR emits partials.

2. Conversation OS extracts goal/constraints; detects it’s a multi‑step task.

3. Planner drafts a plan: search flights → filter by price/time → propose options → hold seat → create calendar event → set price‑watch.

4. Router assigns TravelAgent, SchedulerAgent, WatcherAgent; Model Router picks optimal models per step.

5. TravelAgent calls connectors (flight APIs); Tool‑Calling Engine enforces schemas, retries on transient errors.

6. FlowAI speaks results as they arrive (streaming TTS), accepts barge‑in (“prefer non‑stop”).

7. On approval, SchedulerAgent writes to Calendar (scoped token), WatcherAgent creates a recurring job on the Workflow Engine.

8. All actions logged to Audit, memories update (preferences: “morning, non‑stop, budget ≤€300”).

9. If later a cheaper fare appears, WatcherAgent triggers a compensating workflow to rebook (policy‑gated).

   What Makes It “Impressive” in Practice

   • Voice‑native orchestration: true barge‑in + streaming plan execution, not “voice over a chatbot.”

   • Deterministic multi‑agent control: plans are graphs you can inspect, test, and replay.

   • Enterprise‑proof: least‑privilege connectors, auditable actions, PII controls, tenant isolation.

   • Real‑time at scale: sub‑second first response, parallel tool calls, graceful failover.

   • Living memory: the system actually learns your org’s processes and executes them consistently.