There is no decision tree to draw. The agent is given a personality, knowledge, and tools — your ERP's APIs, your company's knowledge base, your official channels — and it chooses what to do on every turn. Including concluding that nothing needs to be done.
Memory and context for autonomous agents: patent filed with the USPTO in 2025. Meta Business Partner, AWS Partner, and NVIDIA Inception.
Programs Hal-AI takes part in. Trademarks belong to their respective owners.
An airline's operation and the WhatsApp line of a neighborhood store have very different demands. The engine is the same; the front door is not.
Agents that handle service at scale and orchestrators that run business routines across your systems, with a full audit trail of every run.
The agent serves your customers and reports back to you. Pre-trained on your company website, available 24/7, by text, audio, and image.
Flow-based automation works as long as the customer follows the script. An agent with memory and tools does not depend on that.
This is not a chatbot running a flow. It is an operational agent trained to work.
USPTO2025
Patent Autonomous Agent Memory Framework Inspired by Human Cognition
Hal-AI's core technical asset is a memory and context framework for autonomous agents, inspired by human cognition. In plain terms, it addresses three things: the agent's short-term and long-term memory, the retrieval of the context that actually matters at that moment, and task continuity from one conversation to the next.
That is what makes it exclusive. It is the difference between an agent that holds the thread of the conversation — remembering who the customer is, what was already agreed, and what is still open — and an automation that starts over at every message.
In practice: history is vectorized through embeddings, a background worker extracts stable facts and customer traits, and the agent carries a sense of time. The same technology powers text-based service, webchat, real-time voice, and the orchestrator agents — which is why switching channels does not mean reconfiguring the bot.
The framework is already applied by companies in logistics, aviation, healthcare, and banking.
From the agent talking to your customer to the orchestrator running the business routine at six in the morning.
A master agent with no channel of its own. It wakes up on schedule, queries your ERP through its API, decides what needs to happen, and talks in natural language to the channel agents so they handle each customer one by one. It publishes analytical documents with tables and charts and emails them to a closed list of recipients. Every run has an auditable history, a simulation mode, and a stop button.
The agent does not start from scratch with every conversation. Vectorized history through embeddings, episodic memory of stable facts, customer traits, and a sense of time.
It is the same technology on WhatsApp, on webchat, on voice, and in the orchestrator — and it is the subject of the patent filed with the USPTO. The memory screen shows what is stored per channel and lets you clear it.
The in-house assistant for whoever runs the business: it queries your own company data in read-only mode, answers with a table and a chart on screen, and exports to PDF. Managers assign the task, close the page, and get a notification when the result is ready.
A name, a personality, a voice, and real tools: send media and interactive options, request a location, create a WhatsApp template, search the Knowledge Vault, read a website, schedule a task, hand off to a human.
Controls designed for autonomous agents: curated, parameterized queries instead of free-form SQL, an allowlist of what the agent may do, a closed recipient list checked twice before anything is sent, and refusal of an incomplete document — never a silent trim.
The REST APIs you already have become tools for the agent: register the endpoint, the authentication headers, and a description, and it starts querying and acting in that system mid-conversation — ERP, medical records, order management. There is per-agent support for MCP servers, an importer that reads the source documentation and lists the endpoints, and a versioned public API — with scopes, pagination, and idempotency keys — that even exposes triggering and monitoring Squad runs.
The six pieces do not live on separate screens: whoever administers the account opens the console and sees everything on a single dashboard — how many channels are up, how many agents are answering, how many conversations are open, and which Squads wake up today.
The dashboard is the first screen after login. The four cards at the top answer at a glance whether the operation is up: channels connected, agents active, conversations open, and Squad runs due for the day. The chart compares the volume of the last seven days, with the weekend dimmed so it does not distort the reading. Below it, the alert strip gathers what needs attention right now — the routine that is running, the campaign the guardian slowed down, and the work the Copilot has just delivered.
On one side, the people who buy, book, ask, and complain. On the other, the people who need to know what happened without opening a single dashboard.
This is not a chatbot running a flow. It is an operational agent trained to work.
Connecting to inventory, calendars and back-office systems happens through the integration of your company's APIs.
Messaging, website chat, and real-time voice all run on the same prompt, the same tools, and the same memory.
Official numbers through the Meta Cloud API, with assisted onboarding, WABA activation, and templates created and approved inside the platform.
Embed, shareable link, and QR code. Attachments, photos, recorded audio, and handoff to a human — and the conversation lands in the same CRM.
The customer calls and the AI answers in real time: it talks, queries the same systems, transfers the call, and hangs up. Every call is transcribed.
Reports and documents published by the agents arrive by email; a browser notification tells you when the work you asked for is ready.
The four front doors all lead to one place. In the inbox, WhatsApp threads, webchat conversations, and calls answered by the AI show up in the same queue — and every row says who is handling it at that moment: the agent or a person on your team.
| Conversation | Channel | Who is answering | Last message | Waiting |
|---|---|---|---|---|
| Aurora Store | Person · Marina | “order 4471 arrived with the wrong item” | 6 min | |
| Vitória Clinic | Webchat | Agent · Scheduling | “can I move Thursday's follow-up?” | 0 min |
| Meridian Freight | Voice | On a call | Route 12 · transcript being recorded | 2 min |
| Horizon School | Agent · Enrollment | “is there a spot in Saturday's trial class?” | 0 min | |
| Aurora Store | Webchat | In the queue | “do you deliver same day?” | 1 min |
Up top, the whole queue and the pills counting where the open conversations came from — WhatsApp, webchat, and voice. There is only one table: each row shows the customer's channel, who is handling the conversation at that moment, and how long they have been waiting. The amber banner spells out in words what the first row already flags: that chat was taken over by a person, and the AI is paused in that one thread only. The call in progress sits in the same list, transcribed turn by turn.
Nobody has to open three dashboards to see how big the queue is, or find out too late that the agent and a person both replied in the same chat: assigning the conversation is what pauses the bot, and the badge naming who is answering stays written on the row. Closing it hands the conversation back to the AI with the full history — including whatever the person agreed to along the way.
The agent that answers on WhatsApp is the same one that answers in your website chat and the same one that picks up the SIP call — same prompt, same tools, and the same long-term memory covered by our patent filing with the USPTO. That is why a customer who wrote in the webchat on Tuesday and came back on WhatsApp today continues the conversation instead of starting over, and why adding a channel is not rebuilding the bot all over again.
Aviation, pharmacy retail, and payroll-deducted lending — three operations with very different demands on the same platform.
A major Brazilian airline serves passengers over WhatsApp: check-in, disruption handling, and multilingual support in more than 40 languages.
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Hal-AI Agentic integrated with Alpha 7 at one of Brazil's largest chains, with a potential reach of 8,000 pharmacies.
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A partnership with PHNIX Card: payroll-deducted lending with end-to-end journey traceability over the WhatsApp Business API.
See the caseIf the conversation is about operations, systems, and scale, talk to a specialist. If it's about putting an agent to work on your business's WhatsApp today, start with the small-business track.