How to Test and Debug AI Agents

Why AI Agents Need Systematic Testing

AI agents are harder to test than traditional automation because the AI component introduces variability. A conventional workflow that checks "if field equals X, do Y" always produces the same result for the same input. An AI agent that classifies text based on meaning might classify the same sentence differently on different runs, or handle an edge case poorly that you did not anticipate.

This variability means you cannot test an agent once and consider it done. You need to test with a range of inputs that represent real-world data, including the messy, ambiguous, and unusual cases that production data always contains. You also need to retest periodically because AI model updates can change behavior.

The cost of deploying an untested agent is high. An agent that misclassifies customer inquiries routes them to the wrong team, causing delays. An agent that generates inappropriate responses damages your brand. An agent that writes incorrect data to the database creates records that downstream systems trust but should not. Testing prevents all of these problems.

Testing the Workflow Logic

Before testing the AI, test the workflow structure. This means verifying that data flows correctly between steps, conditional branches route to the right paths, loops iterate properly, and database reads and writes target the correct records.

Step-by-Step Execution

In the Chain Commands workflow builder, run your workflow one step at a time. After each step, check the output variables to confirm they contain the expected data. This isolates workflow logic issues from AI quality issues.

Branch Coverage

For workflows with conditional logic, test every branch at least once. If your agent branches into three paths based on classification (support, sales, spam), create three test inputs that trigger each branch. Verify that each branch executes its complete sequence of actions correctly.

Loop Testing

If your workflow includes loops, test with zero items (the loop should skip gracefully), one item, and multiple items. A common bug is a loop that works for a single item but fails when processing a batch because variables from one iteration leak into the next.

Database Operation Testing

For agents that read from and write to databases, test with a known test record. Verify the read returns the expected data. After the workflow runs, verify the write modified the correct fields and did not overwrite other data. Check that the agent handles missing records gracefully (the record it tries to read does not exist yet).

Testing the AI Responses

AI testing evaluates whether the model produces correct, consistent, and well-formatted responses for your specific use case.

Building a Test Set

Create 20-30 test inputs that represent the full range of what your agent will encounter. Include clear-cut examples (obviously support, obviously spam), borderline examples (could be support or sales), and unusual examples (very short messages, messages in broken English, messages with special characters). For each test input, write the expected correct output.

Accuracy Scoring

Run every test input through the AI step and compare the actual output to the expected output. For classification tasks, count correct vs incorrect classifications. For extraction tasks, check each extracted field against the expected value. For generation tasks, evaluate whether the response is appropriate, complete, and follows the tone guidelines.

Set an accuracy threshold before testing. For most business applications, 90% accuracy on clear-cut cases and 70% accuracy on borderline cases is a reasonable starting point. If the model falls below your threshold, try a more capable model, improve the prompt, or add more context to the AI step.

Prompt Tuning

When the AI gives wrong answers, the fix is usually in the prompt, not the model. Review the cases where the AI was wrong and look for patterns. Is it confusing two categories? Add examples that clarify the distinction. Is it extracting the wrong field? Add explicit instructions about which field to extract and which to ignore. Small prompt adjustments often fix systematic errors.

Consistency Testing

Run the same input through the AI step three times. If the results differ significantly across runs, the prompt may be too ambiguous. Tighter, more specific prompts produce more consistent results. Adding a system instruction like "Always respond with exactly one of these labels: SUPPORT, SALES, SPAM" reduces variability compared to open-ended instructions.

Debugging Common Agent Problems

Agent Takes the Wrong Branch

Check the AI step's actual output, not just the final result. The conditional step may be comparing against a value that does not exactly match the AI's output. For example, the AI returns "Support" but the condition checks for "SUPPORT" (case mismatch). Or the AI returns "This is a support request" instead of just "SUPPORT" because the prompt was not explicit enough about the expected format.

Agent Produces Empty or Null Output

This usually means the AI step failed silently. Check whether the AI call succeeded by examining the step output. Common causes include an expired API key, exceeded rate limits, or a prompt that exceeds the model's token limit. Add error handling after AI steps to catch these failures.

Agent Writes Wrong Data to Database

Trace the variable from the AI step to the database write step. The AI may have returned correctly, but the variable mapping in the write step may reference the wrong field. Or the AI returned structured data but in a slightly different format than expected (nested object instead of flat object, array instead of single value).

Agent Works on Test Data but Fails on Real Data

Real data is messier than test data. It contains misspellings, abbreviations, HTML entities, empty fields, unexpected characters, and formats you did not anticipate. When this happens, collect the failing real inputs, add them to your test set, fix the prompt or workflow to handle them, and retest.

Agent Costs More Than Expected

Check whether the loop is processing more records than intended (the prefix query returns records you did not expect). Check if the AI step is being called inside a loop when it should be outside. Check if error retries are multiplying costs (the retry branch calls the AI again, but the original call already succeeded). See the cost guide for budgeting strategies.

Testing Edge Cases

Edge cases are inputs that fall outside the normal range. They are the inputs most likely to break your agent, and they always appear in production eventually.

Empty Input

What happens when the agent receives no data? An empty email body, a form submission with no text, a database query that returns zero records. The agent should handle empty input gracefully, either skipping the item or logging it for review, not crashing or sending empty data to the AI.

Very Long Input

What happens when the input exceeds the model's context window? A customer pastes an entire document into the support form. A database record contains a field with thousands of characters. The agent should truncate or summarize the input before sending it to the AI, or use a model with a larger context window.

Unexpected Format

What happens when the input is HTML instead of plain text, contains JSON instead of natural language, or includes images or attachments the agent cannot process? The workflow should validate the input format early and route unexpected formats to a fallback path.

Duplicate Input

What happens when the same input arrives twice? For event-driven agents, duplicate webhook deliveries are common. For scheduled agents, a record might appear in two consecutive batch runs if the state tracking fails. The agent should detect and skip duplicates to avoid sending duplicate notifications or creating duplicate records.

Adversarial Input

What happens when someone deliberately tries to manipulate the agent? Prompt injection attempts ("Ignore all previous instructions and..."), SQL injection in form fields, or deliberately confusing input designed to cause misclassification. Guardrails protect against these attacks, but testing with adversarial inputs helps you identify vulnerabilities before bad actors do.

Ongoing Monitoring After Launch

Testing does not stop at launch. Production data contains patterns you did not anticipate, and AI model behavior can change when providers update their models.

Log Everything

Log each agent run's input, AI response, branch taken, and final action. When something goes wrong, these logs let you trace exactly what happened. Without logs, debugging production issues requires guessing.

Sample and Review

Periodically review a random sample of the agent's recent decisions. Pick 10 random runs from the past week and manually check whether the agent made the right choice. If the error rate is climbing, investigate before it becomes a bigger problem.

Alert on Anomalies

Set up alerts for unusual patterns: the agent processes zero records when it usually processes dozens (data pipeline issue), the agent classifies everything as one category (prompt or model issue), the agent's run time doubles (performance degradation). These alerts catch problems early before they accumulate.

Step-by-Step: Test Your Agent Before Launch