Vivollo
Agent runtime

An agent that acts — not just answers.

Vivollo runs a real multi-step agent: it looks up orders, searches your catalog and docs, remembers the customer, and takes the next step — end to end, not a scripted reply.

Across every channel · 24/7 · escalates only when it matters

Vivollo AssistantOnline
My order #10482 still isn't here — can you check?
get_order· #10482shipped
It shipped Tuesday and is out for delivery today — here's your tracking link.
rag_search· refund_policy30-day windowupdate_memory· customersaved
If it doesn't arrive by tomorrow, you're covered by our 30-day return policy — want me to start a return?
Resolved in 14s · no agent needed
8+ live tools·persistent memory·provider failover·token streaming
01Tool use8+ tools

It uses tools, not scripts

The agent calls real functions to get the job done — order and shipping lookups, catalog search, document retrieval, customer context. Each step is a decision, not a branch you pre-wired.

  • get_orderorder + shipping
  • search_cataloglive inventory
  • rag_searchyour docs
  • update_memorycustomer
  • resolveclose or hand off
02Memoryload / update

It remembers the customer

Vivollo carries context across messages and sessions — who the customer is, what they bought, what they asked before — so conversations pick up where they left off instead of starting cold.

  • customer identity
  • past orders
  • preferences
  • open tickets
03Resilienceauto-failover

It stays up under load

Provider-agnostic by design: Vivollo routes across OpenAI and Gemini with automatic failover and cost-tuned model tiers, so a single provider hiccup never takes your support offline.

  • OpenAI ⇄ Gemini
  • automatic failover
  • model tiersnano → smartest
  • token streaming

The agent loop

How a single message gets resolved

Every turn runs the same loop — perceive, decide, act, observe — repeating until the request is actually done.

  1. 01

    Read the message

    Parse intent and entities from the customer's message, in any of your channels and languages.

  2. 02

    Recall context

    Load memory and customer context — past orders, preferences, the thread so far.

  3. 03

    Decide the next step

    Choose whether to answer, call a tool, ask a question, or hand off — grounded in your rules.

  4. 04

    Call a tool

    Run the function — look up an order, search the catalog, retrieve a doc, update memory.

  5. 05

    Read the result

    Take the tool's output back into context and re-evaluate what's still needed.

  6. 06

    Resolve or hand off

    Close the loop with a grounded answer and action — or pass full context to a human.

The loop repeats until resolution — or a clean handoff to your team.

Ready to meet your AI agent?

Book a demo and we'll build a working agent on your real data — across WhatsApp, Instagram and your website. Live in days.