Everything you need to understand AI agents, master the Atlastix platform, and deploy your digital workforce
We've organized content from foundational concepts to advanced technical topics. Start wherever makes sense for your role.
Start here if you're new to AI agents. Learn what they are, how they differ from chatbots and RPA, and how they work.
Understand the building blocks: agents, subagents, integrations, tools, knowledge base, context, and more.
See the actual interface with guided tours showing how to configure agents, set up integrations, and more.
Advanced technical topics: decision-making, tool orchestration, knowledge architecture, and security.
Jump into any section below to start learning, or scroll through them all for a comprehensive understanding of the Atlastix platform.
Understanding the fundamentals of AI agents and how they're revolutionizing business automation
An AI agent is an intelligent digital employee that can understand instructions, make decisions, and take actions across multiple systems autonomously. Unlike simple automation tools, AI agents combine language understanding, contextual awareness, and the ability to use various tools to complete complex business tasks.
Think of an AI agent as a team member who can read emails, access databases, update spreadsheets, communicate with other systems, and make intelligent decisions based on your business rules—all without human intervention except where you require approval.
Agents don't just answer questions—they actually perform work. They can create records, send emails, update databases, generate reports, and execute multi-step workflows across your entire technology stack.
Agents understand the broader context of their tasks. They can read through documents, understand relationships between data, and make informed decisions based on your business policies and historical patterns.
Agents work across all your tools seamlessly. They can pull data from your CRM, check your ERP, update your project management tool, and send notifications—all in a single workflow without manual intervention.
Agents operate within defined boundaries. You set approval thresholds, define what data they can access, require human review for critical actions, and maintain complete audit trails of every decision and action.
Understanding the building blocks of Atlastix—from agents and integrations to knowledge bases and prompt engineering
Your Digital Employees
Agents are the core of the Atlastix platform—autonomous AI workers that can understand instructions, make decisions, and take actions across multiple systems.
Each agent has a specific role and expertise area, like an Accountant Agent or SOC Analyst Agent
Agents are configured with prompt engineering to define their behavior, personality, and decision-making patterns
They maintain context across conversations and tasks, remembering previous interactions
Agents can call on subagents for specialized tasks they aren't equipped to handle
Every agent action is logged and auditable for compliance and transparency
You control what data agents can access and what actions require human approval
Xero Accountant Agent: Autonomous finance analyst that processes and loads invoices
SOC Triage Analyst: Security agent that correlates alerts and drafts incident responses
Sales Ops Assistant: Maintains CRM data quality and generates proposals
Agents use tools to interact with integrations (both MCP and Data Ingest), guided by prompt engineering and informed by the knowledge base. They maintain context throughout their work and can delegate to subagents for specialized tasks. MCP provides the universal protocol for real-time tool access, while Data Ingest streams enterprise data for analysis and decision-making.
Explore the actual Atlastix interface with guided tours showing how to configure and deploy your AI agents
The Agent Dashboard is your central hub for managing all your AI agents. Browse templates, create new agents, and configure existing ones.
Screenshot: Agent Management Dashboard
Interface preview for this section
View all your agents in one place with grid or list view
Each agent shows its type (AGENT tag) and current status
Quick access to Configure and Chat with any agent
Import/Export capabilities for sharing agent configurations
Now that you understand the interface, try it yourself. Create your first agent, connect your tools, and watch your digital employee get to work.
Advanced technical concepts for understanding how AI agents work under the hood
Understanding the cognitive architecture behind AI agent decision-making processes.
AI agents make decisions through a combination of language model reasoning, knowledge retrieval, and rule-based constraints. The process is transparent and auditable at every step.
Task Understanding: The agent parses the request to identify intent, entities, and required actions
Context Retrieval: Relevant information is pulled from knowledge base, conversation history, and connected systems
Planning: The agent creates a step-by-step execution plan considering available tools and business rules
Constraint Checking: Business rules, approval requirements, and security policies are validated
Execution: Actions are taken with each step logged for transparency
Verification: Results are checked against expected outcomes and exceptions are flagged
Prompt Engineering: Core instructions defining the agent's role and decision-making approach
Knowledge Base: Organizational policies, SOPs, and historical patterns inform decisions
Business Rules: Hard constraints like approval thresholds and data access policies
Context: Current conversation state, related entities, and task history
Tool Availability: Which actions the agent is capable of performing
Confidence Levels: The agent can express uncertainty and request human guidance
When faced with ambiguity, agents can ask clarifying questions before proceeding
Low confidence decisions can be flagged for human review automatically
Agents explain their reasoning, making it easy to understand why a decision was made
Fallback patterns ensure graceful degradation when optimal solutions aren't available
"Process invoice #12345" → Intent: process_invoice, Entity: invoice_12345
Pull invoice data from Xero, check PO existence, verify budget availability
Amount $5,000 > $1,000 threshold → requires approval
Validate invoice → Route to CFO → Update status → Send notification
Each action completed with logging and error handling
Summary: Invoice routed to CFO for approval, notification sent
Now that you understand the platform, it's time to see it in action and start building your digital workforce.