Getting Started with Agent Loops

This guide walks you through creating your first Agent Loop - an autonomous AI workflow that can use tools and complete tasks without user interaction.

Simple Example: Daily Weather Report

Let’s create an agent that fetches weather data and sends a report.

Create Agent Loop

Navigate to Agent Loops and click Create Agent Loop.

Name It

Name: “Daily Weather Reporter”

Write System Prompt

You are a weather reporter. Your job is to fetch current weather
data for San Francisco and create a brief summary report.

Use the HTTP tool to call a weather API, then format the results
into a readable summary including temperature, conditions, and
a brief forecast.

Set User Message

Get today's weather for San Francisco and create a summary report.

Add MCP Server

Add the HTTP Client MCP server so the agent can call weather APIs:

Server Type: HTTP Client
URL: (automatically configured)

Add Callback (Optional)

If you want to receive the report via webhook:

Callback URL: https://your-domain.com/weather-reports
Headers: Any authentication needed

Run

Click Create then Run to execute the agent loop.

What Happens

When you run the agent loop:

Agent Reads Instructions: Processes system prompt and user message
Plans Approach: Decides to use HTTP tool to call weather API
Calls Tool: Makes HTTP request to weather service
Processes Response: Extracts temperature, conditions, forecast
Generates Report: Creates formatted summary
Sends Callback: Delivers report to your webhook (if configured)
Completes: Marks execution as successful

Example Output

The agent might produce:

Weather Report for San Francisco - January 15, 2024

Current Conditions:
- Temperature: 62°F (17°C)
- Conditions: Partly Cloudy
- Humidity: 65%
- Wind: 10 mph SW

Forecast:
Expect partly cloudy skies throughout the day with temperatures
reaching a high of 65°F. No precipitation expected. A pleasant
day ahead!

More Complex Example: Database Report

Create an agent that queries a database and generates a report.

Create Agent Loop

Name: “Daily Sales Report Generator”

System Prompt

You are a sales analyst. Your task is to:

1. Query the sales database for yesterday's data
2. Calculate these metrics:
   - Total revenue
   - Number of orders
   - Average order value
   - Top 5 products by revenue
3. Format results as a professional report
4. Include insights about any notable trends

Use the database tool to execute queries.

User Message

Generate yesterday's sales report and identify any trends.

Add Database MCP Server

(Coming soon - use HTTP to query API for now)

Configure Callback

Send results to your team’s Slack channel or email system.

Agent Loop Patterns

Pattern 1: Data Fetcher

Purpose: Retrieve data from external sources Components:

HTTP MCP server
Clear data source instructions
Output formatting guidelines

Example: Fetch stock prices, weather, news

Pattern 2: Data Processor

Purpose: Transform or analyze data Components:

File system MCP server
Processing instructions
Output format specification

Example: Analyze CSV files, process logs, summarize documents

Pattern 3: Report Generator

Purpose: Create scheduled reports Components:

Data source access (HTTP/Database)
Report template in system prompt
Callback for delivery

Example: Daily sales reports, weekly summaries, monthly analytics

Pattern 4: Monitoring Agent

Purpose: Check systems and alert on issues Components:

HTTP MCP for API checks
Conditional logic in prompt
Callback for alerts

Example: Website uptime, API health, error rates

Iterative Development

Agents use multiple reasoning steps:

Iteration 1: "I need to fetch weather data. I'll call the API."
→ Uses HTTP tool

Iteration 2: "I got the data. Now I'll extract key information."
→ Processes response

Iteration 3: "I have the information. I'll format it as a report."
→ Generates output

Iteration 4: "Report complete. I'll send it via callback."
→ Completes task

Set max iterations based on task complexity:

Simple: 5 iterations
Medium: 10 iterations
Complex: 20+ iterations

Testing Your Agent

Start Simple

Begin with basic instructions and one tool.

Run Manually

Execute and review the output.

Check Logs

Review iteration logs to see reasoning process.

Refine Prompt

Adjust system prompt based on results.

Add Complexity

Gradually add more tools and steps.

Test Edge Cases

Try with different inputs and scenarios.

Common Mistakes

Vague Instructions

Bad:

Analyze the data.

Good:

Query the database for yesterday's sales. Calculate total revenue
and average order value. Format as a JSON report with these fields:
{date, total_revenue, order_count, average_order_value}.

Too Many Tools

Problem: Providing unnecessary MCP servers confuses the agent. Solution: Only include tools needed for the specific task.

Insufficient Iterations

Problem: Agent runs out of iterations before completing. Solution: Increase max iterations or simplify the task.

No Error Handling

Problem: Agent fails when API returns an error. Solution: Include error handling instructions in system prompt:

If the API returns an error, try once more. If it fails again,
return a message explaining the issue.

Best Practices

Explicit Steps

Break tasks into numbered steps in system prompt

Expected Output

Describe exactly what format you want

Error Handling

Tell the agent how to handle failures

Test Thoroughly

Run multiple times with different scenarios

Next Steps

MCP Servers

Learn about available tools

Callbacks

Set up result webhooks

API Reference

Trigger agents programmatically

Get Started

Bots

Agent Loops

MCP Servers

Use Cases

Simple Example: Daily Weather Report

What Happens

Example Output

More Complex Example: Database Report

Agent Loop Patterns

Pattern 1: Data Fetcher

Pattern 2: Data Processor

Pattern 3: Report Generator

Pattern 4: Monitoring Agent

Iterative Development

Testing Your Agent

Common Mistakes

Vague Instructions

Too Many Tools

Insufficient Iterations

No Error Handling

Best Practices

Explicit Steps

Expected Output

Error Handling

Test Thoroughly

Next Steps

MCP Servers

Callbacks

API Reference

​Simple Example: Daily Weather Report

​What Happens

​Example Output

​More Complex Example: Database Report

​Agent Loop Patterns

​Pattern 1: Data Fetcher

​Pattern 2: Data Processor

​Pattern 3: Report Generator

​Pattern 4: Monitoring Agent

​Iterative Development

​Testing Your Agent

​Common Mistakes

​Vague Instructions

​Too Many Tools

​Insufficient Iterations

​No Error Handling

​Best Practices

Explicit Steps

Expected Output

Error Handling

Test Thoroughly

​Next Steps

MCP Servers

Callbacks

API Reference

Simple Example: Daily Weather Report

What Happens

Example Output

More Complex Example: Database Report

Agent Loop Patterns

Pattern 1: Data Fetcher

Pattern 2: Data Processor

Pattern 3: Report Generator

Pattern 4: Monitoring Agent

Iterative Development

Testing Your Agent

Common Mistakes

Vague Instructions

Too Many Tools

Insufficient Iterations

No Error Handling

Best Practices

Next Steps