> ## Documentation Index > Fetch the complete documentation index at: https://docs.alysio.ai/llms.txt > Use this file to discover all available pages before exploring further. # Agent Builder Setup > Design and deploy AI Revenue Agents ## Agent Builder Setup Agent Builder Setup allows organizations to design and deploy AI Revenue Agents that automate operational workflows across the revenue stack. The Agent Builder is the configuration environment within Alysio where administrators and revenue operations teams define how agents monitor signals, interpret operational data, and execute actions across connected systems. Using the Agent Builder, organizations can create agents tailored to specific operational objectives such as pipeline monitoring, deal risk detection, account engagement tracking, or meeting preparation. Once deployed, these agents continuously monitor operational activity and perform automated workflows when defined conditions are met. *** ## Definition The Agent Builder is the configuration interface used to create and manage AI Revenue Agents within the Alysio platform. Through the Agent Builder, users define: Agent objectives\ Signal triggers\ Execution conditions\ Workflow actions\ System integrations used during execution This configuration allows organizations to automate operational processes based on revenue intelligence signals. *** ## Purpose of the Agent Builder Revenue teams manage many operational processes that rely on monitoring pipeline activity, engagement patterns, and account signals. Without automation, identifying these events and responding consistently often requires manual monitoring and follow-up. The Agent Builder allows revenue teams to automate these processes by configuring agents that continuously monitor operational data and respond when defined conditions occur. This automation improves operational consistency and reduces the need for manual monitoring. *** ## Core Components of the Agent Builder The Agent Builder organizes agent configuration into several components. ### Agent Objective The objective defines the operational purpose of the agent. Examples include: Monitoring stalled opportunities\ Identifying accounts with declining engagement\ Preparing executive briefs before customer meetings\ Detecting renewal risk signals The objective determines the type of signals and workflows associated with the agent. *** ### Signal Triggers Signal triggers determine when the agent should activate. These triggers originate from the platform’s Signals Engine, which monitors operational data across connected systems. Common signal triggers include: Deal stagnation\ Engagement decline\ Pipeline coverage gaps\ Executive stakeholder changes\ Renewal milestone signals When one of these signals occurs, the agent evaluates whether the defined workflow should run. *** ### Execution Conditions Execution conditions define the operational context required before the agent performs its actions. Examples include: Opportunity stage requirements\ Deal size thresholds\ Account segmentation filters\ Time-based conditions such as renewal windows These conditions ensure that workflows execute only in appropriate operational scenarios. *** ### Workflow Actions Workflow actions define the operational steps the agent performs when triggered. Examples include: Creating follow-up tasks in CRM systems\ Sending notifications to account owners or managers\ Generating executive briefs for upcoming meetings\ Posting alerts to Slack channels\ Updating opportunity records These actions are executed through the platform’s Execution Engine. *** ### System Integrations Agents interact with connected revenue systems when executing workflows. Examples include: CRM platforms such as Salesforce and HubSpot\ Communication platforms such as Slack\ Calendar and email systems\ Intelligence providers such as ZoomInfo These integrations allow agents to perform operational actions across the revenue stack. *** ## Agent Creation Process Creating an agent through the Agent Builder typically involves several steps. ### Step 1: Define the Agent Objective The administrator defines the operational goal of the agent. Examples include: Monitoring pipeline stagnation\ Detecting declining engagement across key accounts\ Preparing executive briefs before scheduled meetings *** ### Step 2: Select Signal Triggers Next, the administrator selects the signals that should activate the agent. These signals are detected by the Signals Engine and may include engagement changes, pipeline activity, or account-level events. *** ### Step 3: Configure Execution Conditions Execution conditions are defined to ensure the agent operates within the intended operational scope. Examples include opportunity stage filters or deal value thresholds. *** ### Step 4: Define Workflow Actions The administrator configures the operational actions the agent should perform once trigger conditions are satisfied. Actions may include notifications, CRM updates, or workflow initiation. *** ### Step 5: Deploy the Agent Once configuration is complete, the agent can be deployed within the workspace. After deployment, the agent continuously monitors operational signals and executes workflows when the defined conditions occur. Administrators can review agent activity through the platform’s operational logs. *** ## Example Agent Workflow A revenue operations team creates an agent designed to detect stalled enterprise deals. Trigger signal\ Deal stagnation detected by the Signals Engine. Execution conditions\ Opportunity stage equals Proposal\ Deal value greater than \$100,000. Workflow actions\ Create a follow-up task for the account owner\ Send a Slack alert to the sales manager\ Generate a pipeline review summary. This automation ensures that high-value deals showing stagnation signals receive immediate attention. *** ## Operational Impact Using the Agent Builder allows organizations to automate operational processes that would otherwise require manual monitoring. Common benefits include: Reduced administrative workload for revenue teams Faster response to pipeline risks Improved consistency in operational workflows Better coordination between sales, revenue operations, and leadership teams By automating these processes, organizations can focus more on strategic decision making and customer engagement. *** ## Platform Data Flow Agent workflows created through the Agent Builder operate through several platform components. Connected Revenue Systems\ ↓\ Alysio Signals Engine\ ↓\ Agent Builder Configuration\ ↓\ AI Revenue Agents\ ↓\ Execution Engine\ ↓\ Operational Actions Across Systems Diagram Alt Text Diagram showing how signals detected from connected revenue systems activate AI Revenue Agents configured through the Agent Builder, triggering automated workflows across CRM and collaboration platforms. *** ## Summary Agent Builder Setup allows organizations to configure AI Revenue Agents that monitor operational signals and automate revenue workflows. By defining objectives, signal triggers, execution conditions, and workflow actions, revenue teams can deploy agents that continuously monitor operational activity and respond automatically when important events occur. This capability enables organizations to scale revenue operations while maintaining visibility and control over automated processes.