How AI Agents Use Workflows and Conditional Logic

What a Workflow Is

A workflow is a series of steps executed in order. Each step performs one action: read data, call an AI model, send a message, update a database, check a condition, or call an external API. Steps connect to each other, and data flows from one step to the next. The visual workflow builder in Chain Commands lets you create these workflows by dragging and connecting steps on a canvas.

For AI agents, the workflow defines the agent's entire behavior. The first step typically gathers data (the observe phase). Middle steps process that data with AI (the decide phase). Final steps take action based on the AI's output (the act phase). More complex agents have multiple decision points and action branches throughout the workflow.

Conditional Logic: Making the Agent Branch

Conditional logic is what makes an agent intelligent beyond the AI model itself. After the AI analyzes data and returns a result, a conditional step checks that result and routes the workflow down different paths. Without conditional logic, every piece of data would follow the same path, which limits what the agent can do.

Simple Conditions

The most basic condition checks a single value. If the AI classified an email as "SUPPORT", go to the support path. If it classified it as "SALES", go to the sales path. If it classified it as "SPAM", go to the discard path. Each path has its own set of action steps.

Compound Conditions

More sophisticated agents combine multiple conditions. A lead qualification agent might branch based on both the AI's score AND the lead's source: "If score is above 8 AND source is the website, send immediate SMS alert. If score is above 8 AND source is a partner referral, send email notification. If score is below 5 regardless of source, add to nurture campaign."

Threshold-Based Conditions

When the AI returns a numerical value (confidence score, sentiment rating, priority level), use threshold conditions to create graduated responses. A sentiment analysis agent might branch differently for scores above 0.8 (very positive), between 0.3 and 0.8 (neutral), and below 0.3 (negative). See How to Use Conditional Logic in Workflows for detailed examples.

Using Variables Across Steps

Workflow variables carry data between steps. When the AI extracts a customer name in step 2, that name is available as a variable in step 5 when you compose a personalized message. When a database query returns a record count in step 3, that count is available in step 4 when you check a condition.

Variables make agents dynamic. Instead of hardcoding values, each step uses data produced by previous steps. The same workflow handles different inputs correctly because the variables change while the workflow structure stays the same.

Looping Through Data

Many agents need to process multiple items: a batch of emails, a list of database records, or a set of form submissions. Loop steps let the workflow iterate through a collection, running the same processing logic on each item.

A data processing agent might query the database for all unprocessed records, then loop through each one, sending it to the AI for classification and taking the appropriate action. Each iteration processes one record, and the loop continues until all records are handled.

Be mindful of costs when looping with AI calls. Each iteration that calls an AI model incurs a credit charge. Processing 100 records with GPT-4.1-mini costs 200-400 credits. Consider using the cheaper GPT-5-nano for bulk processing where the decision is straightforward.

Error Handling in Agent Workflows

Real-world agents encounter errors: API calls fail, database queries return unexpected results, AI models return responses that do not match the expected format. Robust agents handle these situations gracefully instead of crashing.

Add error handling branches to your workflow for likely failure points. If an API call fails, log the error and either retry or skip that item. If the AI returns an unexpected response, route to a fallback path. If a database write fails, queue the data for retry. See How to Handle Errors in Automated Workflows for patterns.

Putting It All Together: A Complete Example

Here is how a customer feedback agent uses workflows and conditional logic together:

• Step 1 (Data): Query database for new feedback submissions since last run
• Step 2 (Loop): For each submission, proceed through the following steps
• Step 3 (AI): Send feedback text to AI, ask for sentiment score (1-10) and category (product, service, pricing, other)
• Step 4 (Condition): Check sentiment score
• Step 5a (Score 8-10): Send thank-you email, add to testimonial candidates list
• Step 5b (Score 4-7): Log for review, tag with category for trend analysis
• Step 5c (Score 1-3): Send SMS alert to manager with feedback details, create follow-up task
• Step 6: Mark submission as processed in database

This single workflow handles all three scenarios automatically. The conditional logic ensures each piece of feedback gets the appropriate response, and the loop processes the entire batch without manual intervention.