AI AGENT PLATFORM

One platform to build and operate AI Agents

AutronAI brings the systems around an AI agent into one place — data, documents, Memory, Skills, MCP tools, Automation, and communication channels — so you can build focused capabilities, connect them together, deploy them, and operate them over time.

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One operating layer

AutronAIChatbot

DataAgentsMemoryMCPChannelsAutomation

01Understand02Act03Operate

The operating layer

From a model to an operating AI Agent

A language model can generate a response. An operating AI Agent needs more: the right information, clear instructions, focused capabilities, durable context, access to external systems, a place to meet users, and a way to keep working over time.

Understand

Ground each task in selected structured data, searchable documents, precise instructions, and reusable Memory instead of relying on the model alone.

Act

Give focused Agents the Skills and individually selected MCP tools they need to perform a responsibility without expanding their scope unnecessarily.

Operate

Compose Agents into Chatbots, connect supported channels, schedule Automations, and inspect activity as the system runs.

Context

Understand with the right context

Different kinds of information need different access patterns. AutronAI keeps structured data, documents, and reusable Memory distinct so each can be attached at the level where it belongs.

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Structured data

Query the approved slice

Connect a database or upload supported CSV and Excel data, then scope a Structured Data / SQL Agent to the tables and columns it is allowed to use.

Documents

Search unstructured sources

Process documents as searchable reference sources so a Document Agent can retrieve relevant material for its answers.

Memory

Reuse context across conversations

Store durable context separately from Agent instructions. Memory is attached at the Chatbot level, where it can support the composed conversational experience.

Focused agents

Give Agents focused capabilities

AutronAI separates responsibilities into focused Agents. Each type has a clear role and receives only the resources that match that role, making the overall system easier to understand and evolve.

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Custom Agent

Uses tailored instructions and selected Skills for a specific responsibility that does not require a dedicated data, document, or tool workflow.

Structured Data / SQL Agent

Works with selected structured sources and respects the configured table and column scope when answering data questions.

Document Agent

Finds and uses relevant information from the document sources selected for that Agent.

Tool Agent

Uses individually selected tools from an installed MCP server to perform external actions within its configured responsibility.

Reusable behavior

Extend behavior with Skills

Skills are reusable instructions and capabilities that help an Agent approach a task consistently. They stay separate from external tools: a Skill shapes behavior, while an MCP tool provides an external action.

Reusable

Define useful guidance once and attach it where the same behavior is needed instead of copying instructions between Agents.

Focused

Keep each Skill centered on a recognizable capability so its purpose and effect remain clear.

Composable

Combine selected Skills with an Agent's own instructions and resources to build up behavior without creating one oversized prompt.

External actions

Connect external capabilities with MCP

MCP gives Tool Agents controlled access to external capabilities. Install or connect a server using the authentication method it supports, inspect the tools it exposes, and choose only the individual tools required by the Agent.

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Connect a server

Install or configure an MCP server and complete credentials or OAuth when that connection supports it.

Inspect available tools

Review the synchronized tool list and descriptions before granting an Agent access.

Select tools for a Tool Agent

Attach individual tools to the Tool Agent that needs them rather than treating the entire server as one broad capability.

Composition

Compose capabilities into a Chatbot

An Agent is a focused capability. A Chatbot is the higher-level conversational composition that brings selected Agents and Memory together. Chatbot Studio makes that composition visible so you can inspect what the Chatbot can use before connecting it to a channel.

Agents carry specialist roles

SQL, document, custom, and tool behavior stays inside the configured Agent instead of attaching those resources directly to the Chatbot.

Memory carries durable context

Attach the reusable Memory the conversation needs at the Chatbot level, alongside the selected Agents.

Studio shows the composition

Use Chatbot Studio to see and adjust the Agent and Memory nodes that make up the conversational system.

Scheduled work

Operate beyond a single conversation

Automation lets an eligible private Chatbot execute an instruction once or on a recurring cron schedule. Configure the timezone, manage the schedule, and inspect each run without introducing a separate event-trigger system.

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Schedule deliberately

Choose an eligible private Chatbot, write the instruction, and set either a one-time schedule or a recurring cron expression with its timezone.

Control the lifecycle

Pause and resume an Automation as requirements change, or delete schedules that are no longer needed.

Inspect every run

Use run history to review status, result, timing, delivery information, and errors exposed by the current Automation workspace.

Deployment

Deploy across communication channels

Once a Chatbot is configured, connect that same composition to the supported communication channel that fits the use case. Each Integration routes channel activity to the selected Chatbot.

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Web Widget

Embed the Chatbot on an approved website and configure the widget experience for web visitors.

Messenger

Connect an authorized Facebook Page and route Messenger conversations to the selected Chatbot.

Telegram Bot

Connect a Telegram bot using its supported token flow and assign the Chatbot that should respond.

Zalo OA / Zalo Bot

Connect a supported Zalo Official Account or bot configuration and route messages to the same configured Chatbot.

Operations

See how the system is operating

Configuration is only part of the work. AutronAI provides operational surfaces for understanding requests, scheduled runs, usage, cost, and account balance where those views apply.

Analytics

Review the request, cost, latency, and error trends available in the Analytics workspace across supported filters and time ranges.

Request and Usage History

Inspect request status, timing, model, and usage records in the history view available for the current workspace role.

Automation Runs

Follow scheduled executions through their run status, result, delivery details, response time, and exposed errors.

Usage, cost, and Wallet

Track usage and cost, and use Wallet views where applicable to understand balance and transaction activity.

An extensible path

Start small and extend over time

You do not need to configure every AutronAI capability before an Agent becomes useful. Start with one responsibility and the smallest set of information or tools required to complete it. Add another Agent when a different responsibility emerges. Add Memory when context should persist. Add MCP tools when external actions are needed. Add Automations when work should happen over time. Add channels when the system is ready to meet users where interactions happen.

The goal is not to build one enormous Agent. It is to create an AI system whose capabilities can evolve independently as requirements change.

FAQ

Questions, answered

Frequently asked questions

What is an AI agent platform?

An AI agent platform provides the operating layer around language models: instructions, data, documents, reusable context, tools, composition, deployment channels, scheduled work, and operational views. AutronAI keeps those concerns separate so teams can build focused Agents and compose them into Chatbots instead of placing every responsibility in one prompt.

How are Agents and Chatbots different in AutronAI?

An Agent owns a focused capability, such as working with structured data, retrieving documents, following Skills, or using selected MCP tools. A Chatbot composes one or more Agents with Memory and becomes the unit connected to supported channels or eligible Automation workflows.

Can an AutronAI Agent use company data?

Yes, through the resource type suited to the information. Structured Data Agents use selected database or tabular sources with configured table and column scope. Document Agents retrieve from selected searchable documents. Reusable contextual information belongs in Memory at the Chatbot level.

Does every Agent need MCP tools or Memory?

No. AutronAI is designed for the smallest useful capability set. MCP belongs on Tool Agents that must act through external systems. Memory belongs on a Chatbot when curated context should remain reusable. An Agent can be useful without either when its responsibility does not require them.

How should a team start building on AutronAI?

Start with one concrete responsibility. Choose the matching Agent type, connect only the required resources, test the behavior, and then compose that Agent into a Chatbot. Add Memory, MCP tools, Automation, or additional channels only when the use case calls for them.

Build with AutronAI

Build the system around your AI Agents

Start with a focused Agent and extend it with the data, context, tools, Automation, and channels your use case actually needs.

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