Creating agents
Here’s a rewritten, simplified, and accurate version of Create Your First AI Agent for Workbird (removing unnecessary code blocks and aligning with how your platform actually works):
Create Your First AI Agent
Learn how to build and deploy intelligent AI agents in Workbird to automate complex tasks and workflows.
What Are AI Agents?
AI Agents in Workbird are specialized automation components that can:
Process Information & Make Decisions – Handle complex logic and context.
Use Tools & Integrations – Connect to external APIs, databases, and services.
Maintain Context – Retain information within a workflow when needed.
Execute Multi-step Tasks – Automate complex sequences of work.
Types of AI Agents
Task Agents
General-purpose agents designed to process data, call APIs, and automate business logic.
Browser Agents
Agents designed for web automation such as scraping, filling forms, or simulating user interactions.
Assistant Agents
Conversational or user-facing agents designed for customer support, Q&A, or interactive data retrieval.
Building Your First Agent
Step 1: Go to the Agents Section
Open “Agents” in the Workbird navigation menu.
Click “New Agent”.
Step 2: Configure Basic Settings
Name – Give your agent a descriptive name (e.g., “Lead Enrichment Agent”).
Description – Explain what the agent does.
Step 3: Define Inputs & Outputs
Specify what data the agent expects (e.g., contact info, query parameters) and how its output should look (e.g., structured data, text summary).
Step 4: Add System Instructions
Provide clear instructions describing what the agent should do and how it should respond. Well-defined instructions significantly improve output quality.
Step 5: Add Tools (Optional)
Workbird agents can call tools such as:
Web search services
External APIs
Data lookups (via MCP servers)
Browser automation
Adding tools allows the agent to extend beyond basic AI responses and perform real actions.
Step 6: Test the Agent
Click “Test Agent”.
Provide sample input data.
Review the response and adjust instructions or tools as needed.
Step 7: Deploy the Agent
Once you’re satisfied with its performance, click “Deploy”. Your agent is now ready for use in workflows.
Using Agents in Workflows
Add as an Action – Drag your agent into any workflow step where you need AI-powered processing or decision-making.
Chain Multiple Agents – Use multiple agents in sequence for complex automation (e.g., data processing → scoring → routing).
Conditional Execution – Execute different agents based on workflow conditions (e.g., different agents for enterprise vs. SMB customers).
Advanced Features
Context Retention – Keep memory between steps for more intelligent, contextual automation.
Browser Sessions – Maintain state for web automation tasks (coming soon).
Error Handling – Configure retries and fallback behavior.
Custom Training – Upload examples or domain-specific data to improve performance.
Managing Agents
Performance Monitoring – Track usage, success rates, and execution times.
Collaboration – Share agents across your team with controlled permissions.
Best Practices
Keep Agents Focused – One purpose per agent improves reliability.
Write Clear Instructions – Be explicit about what you want the agent to do.
Validate Inputs – Ensure the data passed to the agent is complete and accurate.
Plan for Errors – Include fallback behavior in case of failures.
Troubleshooting
Agent Doesn’t Respond – Check required inputs and ensure all connected tools are working.
Low-quality Output – Refine instructions or adjust creativity level.
Tool Errors – Confirm API keys and external service availability.
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