Monitor website chats, route queries to specialized AI agents (RAG, calendar, ticket), and automatically respond, retrieve knowledge, schedule meetings, and escalate as needed.
The AI agent sits at the center of your website chat, routing inquiries to specialized sub-agents based on intent. It retrieves knowledge from internal documents with RAG, checks calendars to schedule meetings, and creates support tickets when needed. All interactions are logged, responses are delivered in real time, and the flow scales by adding more sub-agents.
Orchestrates data, actions, and responses across sub-agents.
Route inquiries to the appropriate sub-agent (RAGAgent, calendarAgent, or ticketAgent) based on detected intent.
Retrieve answers from internal docs via RAGAgent and deliver accurate responses.
Check calendar availability through calendarAgent and book meetings when requested.
Generate and forward support tickets to human agents via ticketAgent.
Log interactions and outcomes for auditing and optimization.
Notify users with confirmations or updates in real time.
This AI agent replaces fragmented manual work with a predictable execution flow.
A simple 3-step flow that non-technical teams can adopt.
The Manager AI Agent analyzes the incoming chat to determine whether the user needs information, scheduling, or escalation.
Based on the detected intent, the AI agent routes the task to calendarAgent, RAGAgent, or ticketAgent and passes necessary context.
Collect outputs from sub-agents, craft a unified reply, perform any follow-up actions, and log the interaction.
A realistic chat scenario showing end-to-end automation.
A website visitor asks to schedule a 2:00 PM meeting next Tuesday. The AI agent checks the connected Google Calendar via calendarAgent, books the meeting, and sends a confirmation through the chat. The agent logs the event and creates a follow-up ticket if additional details are needed.
Roles that gain efficiency from end-to-end AI agent chat automation.
Need automatic handling of common inquiries and scheduling without hiring dedicated staff.
Want scalable response quality and faster ticket routing to human agents.
Require seamless meeting booking from website chats to accelerate deals.
Need reliable access to internal docs through RAG for accurate answers.
Benefit from unified logging and audit trails for compliance.
Can capture and route inquiries for follow-up campaigns and feedback.
The AI agent works with familiar tools to execute actions.
Check availability and schedule meetings directly in connected calendars.
Query documents to retrieve accurate answers for user questions.
Generate and dispatch support tickets to the designated team or inbox.
Capture user messages and pass context to the AI agent for routing.
Common practical scenarios that showcase end-to-end automation.
Practical questions about deployment, security, and capabilities.
An AI Agent is an orchestrator that routes user messages to specialized sub-agents (RAGAgent, calendarAgent, ticketAgent) to perform discrete tasks. It leverages an OpenAI model for understanding and a lightweight memory to maintain context. The sub-agents execute specific actions, such as retrieving knowledge, booking calendars, or creating tickets, while the AI Agent compiles a single, coherent response for the user.
Yes. The AI Agent manages state per chat session and routes each message to the appropriate sub-agent without cross-session leakage. It preserves context within each session and can scale to handle concurrent users. Performance depends on the underlying hosting environment and model parameters, which you can tune for latency and throughput.
The AI Agent supports Google Calendar and Outlook/Exchange as calendar backends. It uses secure authentication and token management to check availability and create events on behalf of the user. You can configure the allowed calendars and access scopes. Calendar events created are visible to users in the chat and stored in logs for auditing.
Data is processed in compliance with your configured data handling policies. The AI Agent stores only necessary context for conversation continuity and auditing, and it respects user consent settings. PII is minimized in logs, and access is restricted by role-based permissions. You can enable additional privacy controls and data retention rules.
RAGAgent queries your vector store or document database to fetch relevant articles or sections. The retrieved material is transformed into concise, user-facing responses and cited when possible. If the documentation lacks coverage, the agent can escalate to a human or propose a workaround. The knowledge source is configurable and can be updated without changing the AI model.
Yes. When a ticketAgent is engaged, or if the sub-agent cannot resolve an issue, the AI Agent creates a structured ticket and routes it to the right team. It can attach context from the chat and related logs to speed up triage. Human agents receive notifications with all relevant details, reducing back-and-forth and wait times.
Absolutely. You can tailor prompts, routing rules, and sub-agent capabilities to align with your brand voice and workflows. Custom prompts can steer how the Manager interprets intents and how aggressively it escalates. Ongoing prompts and routing configurations can be refined based on user behavior and performance data.
Monitor website chats, route queries to specialized AI agents (RAG, calendar, ticket), and automatically respond, retrieve knowledge, schedule meetings, and escalate as needed.
