Getting Started with AI Agents
Beam AI agents are intelligent automation units designed to execute structured flows by processing data, making decisions, and triggering actions across various systems. These agents operate within graph-based flows, allowing them to follow predefined paths based on user-defined logic and dynamically execute tasks based on context.
Key Takeaways
AI Agents execute flows by following user-defined paths, completing steps based on task queries and available context.
Agents can be triggered via multiple external events, such as API calls, third-party platform actions (e.g., receiving an email or Slack message), or time-defined events.
They support cross-agent collaboration, enabling information exchange between agents for multi-step automation.
👉 In this guide, you will learn how to create, configure, and optimize AI agents in Beam AI.
Workflow Capabilities
Beyond conversations, every agent can execute structured workflows to automate predefined tasks. This feature is perfect for automating processes that don’t necessarily require direct interaction but still demand accuracy and efficiency.
Workflows: Workflows enable the agent to perform specific tasks through a series of defined steps, integrating tools and data to complete each process seamlessly. You can create and assign workflows to any agent, allowing it to operate independently of conversational settings.
For detailed instructions, refer to the guide: Mastering Workflow Efficiency
Creating & Managing AI Agents
Beam AI provides two ways to create an agent
1️⃣ Preconfigured Agent Templates – These agents come with a predefined flow, allowing for quick deployment.
2️⃣ Custom Agents – Fully configurable agents where users can define unique flows, triggers, and actions to fit specific automation needs.
📌 Navigating the Agent Dashboard
The Agent Dashboard acts as the central hub for managing all agents and performing real-time actions
Agent Templates: Pre-built industry-specific agents (e.g., Email Triage Agent, KYC Agent, Customer Support Agent).
Agent Quick Actions: Execute on-demand tasks by entering a query in the command bar (e.g., “Sort and flag urgent emails”).
Connected Integrations: View workspace-level integrations that agents can utilise for data exchange
🔗 For a detailed guide on setting up your first AI agent, see Creating & Managing AI Agents.
How AI Agents Work in Beam AI
Each AI Agent is designed with core components that enable structured automation:
1️⃣ Flows - The Execution Framework
AI agents in Beam AI do not operate on static workflows—instead, they execute graph-based flows where users define:
Nodes: Individual action steps (e.g., Extract details from an email, Verify order status, Update a database).
Branches: Conditional decision points where the agent selects the appropriate path based on task context.
Exit Conditions: Endpoints where a process concludes, either successfully or requiring escalation.
🔗 For detailed insights, see “Understanding Graph-Based Flows”.
2️⃣ Triggers - Activating an Agent
Agents operate based on events, which determine when a flow should execute:
✅ External Triggers: Events from third-party platforms (e.g., receiving a Gmail message, Slack notification, webhook request).
✅ Manual Execution: Run an agent on-demand through the Beam AI interface.
✅ Scheduled Triggers: Automate tasks at predefined intervals (e.g., sending daily reports at 9 AM).
Expanding Agent Capabilities
Beam AI agents are not limited to isolated tasks—they can dynamically interact, optimize, and collaborate with other agents.
1️⃣ Cross-Agent Communication via “Agent Calling”
Agents can pass data between each other using the Agent Calling Tool, allowing for multi-step automation across different agents.
📌 Example: A Sales Operations Agent can call a Pricing Review Agent, passing order details for approval before finalizing a sale.
2️⃣ Task Execution & Monitoring
The Tasks Page provides real-time visibility into agent performance, including:
Completed ✅
Consent Required 🆗
Input Required ⏳
Errors & Failures ❌ (With diagnostic insights)
🔗 For deeper insights into execution tracking, see “Execution Accuracy & Node Optimization”.
Managing Integrations & Data
Beam AI agents leverage integrations to connect with external platforms and access structured data.
1️⃣ Workspace-Level Integrations
All connected services (e.g., Google Sheets, Gmail, Slack) are accessible to agents within the same workspace.
2️⃣ Memory Management - Retaining Context
Agents store and retrieve information using:
✅ File Uploads (e.g., CSV, PDF, Excel)
✅ Databases & URLs
✅ Text Snippets for Quick Access
Understanding Agent Analytics
Tracking agent performance is key to refining automation strategies. Beam AI provides comprehensive analytics with:
📊 Task Completion Rate – Percentage of successfully executed tasks
📊 Error Rate – Failed executions and diagnostic insights
📊 Runtime Analysis – Task execution time trends
📊 User Feedback Metrics – Qualitative evaluation of agent decisions
