Thanks — this is a great, clear brief. I’ll respond to each of your four areas (orchestration pattern, parallel tool execution, memory management, example architectures) and finish with a concise recommended implementation outline you can adapt. Acknowledgement: you want a low-latency, voice-first, step-by-step assistant that (1) pauses for explicit user confirmation, (2) supports backtracking/clarification without losing place, and (3) calls external tools (ticketing/CRM/etc.) during the flow. Below is a practical approach using LiveKit Agents. 1) Orchestration pattern — how to manage step-by-step + tool calling • Use AgentTask (and TaskGroups) as the primary control construct. ◦ Model each procedural step as an AgentTask so the task “owns” the session until that step is explicitly completed (e.g., user says “Step 1 completed” or triggers a confirm tool). ◦ Tasks give deterministic control flow—better than relying only on LLM chat memory for production workflows. • Keep the LLM responsible for natural language understanding and decision-making but not as the single source of truth for state. • Persist the authoritative procedural state in

session.userdata

(see Memory below). Use the LLM’s context for transient conversational reasoning and for generating user prompts. • Use function tools (annotated tool functions) for external actions; have tasks call those tools when appropriate. This keeps the procedural flow separate from the implementation of tool calls. When to ask a clarifying question first: • If there are multiple implementation routes (e.g., server-side orchestrator vs. per-agent state machine), tell me which one you prefer (fully hosted orchestration or embedded per-agent state) and I’ll give more specific code patterns. Right now the Task +

session.userdata

hybrid is recommended. 2) Parallel tool execution — how to call tools while keeping the user engaged • Run long-running external calls asynchronously so you don’t block speech/TTS generation. ◦ Start the external API call in a background async task (e.g.,

asyncio.create_task

), and immediately confirm to the user with a short acknowledgement: “I’m creating that ticket now — I’ll confirm when it’s done.” ◦ Provide interim feedback (short voice messages, “thinking” sounds, or progress updates) while the background work completes. • Use streaming patterns already present in LiveKit: the pipeline (STT → LLM → TTS) continues producing audio while backend tasks run. • If the tool result will affect the immediate next step, design the flow to: a. Acknowledge to the user that the action started, b. Continue with non-dependent dialog (or ask the next confirmation), c. Once the async tool returns, update

session.userdata

, and push a follow-up utterance with the result (“Ticket 123 created”). • For errors or escalations, provide immediate graceful fallback (e.g., “I’m unable to create that ticket right now — would you like me to try again or escalate?”) while the session state retains where you were in the procedure. 3) Memory management — keeping procedural state and external data synchronized Use a hybrid memory model with clear separation of responsibilities: • session.userdata (authoritative procedural state) ◦ Store deterministic items:

current_step

,

completed_steps

,

step_deadlines

,

ticket_id

,

escalation_status

, any external IDs and flags. ◦ This is your source of truth for “where are we in the procedure” and must be updated atomically when tasks complete or tools return. • agent.chat_ctx (short-lived agent reasoning context) ◦ Use for the LLM’s immediate reasoning and to hold the truncated context necessary for on-the-fly decisions. • session.history (full conversational log) ◦ Use this to reconstruct prior turns for “repeat” or “what did you just say?” requests. ◦ When user asks to repeat/backtrack, consult

session.userdata

for the step index and

session.history

to recover the exact phrasing if you want to replay the earlier instruction. • External store (optional for multi-session resilience) ◦ If sessions may reconnect or survive beyond a single connection, persist

session.userdata

(or a normalized projection of it) in a fast datastore (Redis/DB). ◦ Keep a compact representation in

session.userdata

and write-through to the external store on step transitions. • Synchronization rules ◦ Update

session.userdata

immediately after a tool returns (success or fail). ◦ When multiple async operations might write the same fields, use small atomic update functions or a Task-coordinator that sequences updates to avoid races. ◦ Make LLM outputs idempotent or checkpoint each step so replays/retries do not corrupt state. 4) Example architectures / reference projects to follow • Drive-thru ordering example: demonstrates multi-step workflows, structured function calling, stateful order management — suitable as a template for step-by-step flows and for extracting structured data from user speech. • Multi-agent handoff pattern: demonstrates handing off state and context between agents (intake → specialist) and shows how to preserve

session.userdata

. • Agent Playground / multimodal prototype: good for validating voice-first UX and low-latency interactions before production hardening. If you want, I can point you to relevant example code or walk through one example flow mapped to your ticketing/CRM use case. Recommended implementation outline (practical skeleton) 1. Define a ProcedureState dataclass and attach one instance to

context.session.userdata

. ◦ Fields:

current_step

,

completed_steps

,

ticket_id

,

last_prompt_id

,

in_progress_tasks

, etc. 1. Implement each procedural step as an AgentTask: ◦ Task responsibilities: prompt the user, validate confirmation, call necessary tools, update

session.userdata

, complete or yield to next Task. 1. Expose external APIs as

@function_tool()

functions: ◦ Tools run asynchronously. For long calls, spawn an async worker and immediately return an acknowledgement to user. ◦ On completion, the tool updates

session.userdata

and emits a follow-up reply into the session. 1. Manage backtracking & repetition: ◦ Use

session.userdata.current_step

to identify which step to replay. ◦ Pull the original instruction from

session.history

or regenerate the step instruction from the stored step metadata and replay it. 1. Multi-agent handoff (if needed): ◦ Transfer

session.userdata

to the receiving agent and keep a small transfer summary in

session.history

. 1. Persistence for reconnection: ◦ Optionally persist

session.userdata

to a fast store on each step transition; restore it on reconnect. Short code sketch

@dataclass
class ProcedureState:
    current_step: int = 1
    completed_steps: List[int] = field(default_factory=list)
    ticket_id: Optional[str] = None
    in_progress: bool = False

class ConfirmStepTask(AgentTask[bool]):
    async def run(self, context: RunContext):
        state: ProcedureState = context.session.userdata
        # Prompt user to confirm the current step
        await context.session.generate_reply(f"Please confirm completion of step {state.current_step}.")
        # Wait for a confirmation signal (turn detection + intent)
        # On positive confirmation:
        state.completed_steps.append(state.current_step)
        self.complete(True)

Follow-ups I can help with • Map a specific 5–10 step procedure (you provide steps) into AgentTasks and session.userdata layout. • Example code showing async ticket creation with interim voice feedback and final confirmation. • Patterns for multi-agent escalation (state transfer, human-in-loop handoff). • Data model suggestions for

session.userdata

and persistence to Redis or DB for resilience. If you want the next step, tell me: • Do you prefer a single-agent implementation that orchestrates all steps, or a multi-agent system (intake agent → specialist agents)? • Do you need sample code for a specific external API (a ticketing system or CRM) to demonstrate the async tool pattern? I’ll produce targeted code examples and a sequence diagram for whichever option you pick. Sources: Building multi-agent architectures with LiveKit agents | LiveKit | Tasks and task groups | LiveKit Documentation | External data and RAG | LiveKit Documentation | examples/drive-thru | livekit/livekit_composite | DeepWiki | An open source stack for real-time multimodal AI

you told me to use agenttask and taskgroup can you give me an example about how can I use this structure in my specific scenerio