Logging & Debugging Chatbot Workflows

Logging and debugging are what keep complex chatbot setups predictable, explainable, and fixable. Aimogen provides visibility into what the chatbot did, why it did it, and which part of the workflow was responsible, without turning the system into a black box.

This section explains how to understand chatbot behavior when things don’t go as expected.

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What Gets Logged #

Aimogen can log chatbot activity at multiple levels, depending on configuration.

Typical logged elements include:

• user messages
• AI responses
• trigger evaluations
• workflow execution steps
• appended system prompts
• external action execution
• provider and model used
• timestamps and context

Logging is intentional and configurable. Nothing is logged “just in case” unless you enable it.

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Chatbot Logs vs AI Provider Logs #

Aimogen logs:

• what the chatbot attempted to do
• which rules fired
• which actions ran
• what data was passed internally

AI providers log:

• API calls
• token usage
• errors at the provider level

These are complementary. Aimogen logs explain logic. Provider logs explain execution.

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Where to Find Chatbot Logs #

Chatbot-related logs are accessible through the Aimogen admin interface.

Depending on configuration, you can inspect:

• conversation-level logs
• workflow execution traces
• error messages
• API interaction summaries

Backend (Playground) interactions are logged separately from frontend usage.

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Understanding Workflow Execution Order #

When debugging, it’s critical to understand the order of execution:

1. placement and context checks (frontend only)
2. conversation start logic
3. trigger evaluation
4. conditional checks
5. hardcoded message workflows
6. appended system prompts
7. AI response generation
8. external actions
9. termination or continuation

Logs follow this order. If something didn’t happen, look earlier, not later.

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Debugging Triggers #

When a trigger does not fire, the most common reasons are:

• condition mismatch
• incorrect trigger priority
• conflicting triggers
• trigger scoped too narrowly
• unexpected user state

Logs will usually show:

• whether the trigger was evaluated
• whether conditions passed or failed
• why execution stopped

Never assume a trigger “didn’t run” without checking evaluation logs.

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Debugging Hardcoded Workflows #

If a hardcoded message did not appear:

• check trigger conditions
• confirm workflow order
• verify no earlier workflow terminated the conversation
• confirm the chatbot did not switch control back to AI prematurely

Hardcoded workflows are deterministic. If they didn’t run, a condition blocked them.

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Debugging Appended System Prompts #

Appended prompts are invisible to users, so logs are essential.

Logs can reveal:

• when a system prompt was appended
• how long it remained active
• whether it was overridden
• whether it conflicted with another prompt

If AI behavior changes unexpectedly, check for lingering appended prompts first.

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Debugging AI Responses #

When AI output looks wrong:

• verify the active persona
• check appended system prompts
• confirm model and provider
• inspect conversation context size
• check for truncated history

Most “bad AI behavior” is caused by instructions, not the model.

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Debugging External Actions #

If an external action did not execute:

• check trigger conditions
• confirm required data existed
• inspect payload structure
• check endpoint availability
• review error logs

External actions should always log:

• attempted execution
• success or failure
• error messages

Never rely on silent failures.

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Frontend vs Backend Debugging #

Backend (Playground) debugging:

• simpler user state
• no placement rules
• ideal for logic testing

Frontend debugging:

• affected by caching
• affected by roles and devices
• affected by consent gating
• affected by visibility rules

Always reproduce frontend issues in frontend context.

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Common Debugging Patterns #

Useful questions to ask:

• did the trigger evaluate?
• did conditions pass?
• did a workflow terminate early?
• was the AI even called?
• was a system prompt appended?
• did an external action fail silently?
• did caching affect visibility?

Logs answer these questions directly.

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Performance and Logging #

Logging increases visibility but also:

• uses storage
• may expose sensitive data
• can affect performance at scale

For production sites:

• log what you need
• avoid logging personal data unnecessarily
• rotate or clean logs periodically

Logging should be deliberate, not permanent.

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What Logging Does Not Do #

Logging does not:

• fix broken logic
• validate user input
• prevent misconfiguration
• replace testing
• interpret intent for you

Logs explain behavior; humans fix it.

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Best Practices #

• test workflows in the backend first
• enable logging during development
• disable excessive logging in production
• document trigger intent
• name workflows clearly
• change one thing at a time
• reproduce issues before fixing

Debugging is fastest when changes are incremental.

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Summary #

Logging and debugging in Aimogen give you transparency into chatbot workflows, triggers, and actions. Logs show what happened, in what order, and why. When used correctly, they turn even complex chatbot systems into understandable, maintainable, and reliable components. Most issues are not AI failures—they are logic or configuration issues that logging makes visible.