- What Conversation Control Means
- Why Termination Matters
- How Conversations Are Ended
- Soft vs Hard Termination
- Trigger-Based Termination
- Termination After Lead Collection
- Escalation Instead of Termination
- Conversation Reset
- User-Initiated Termination
- AI-Level Termination Instructions
- What Conversation Control Does Not Do
- Common Mistakes
- Best Practices
- Summary
Conversation control and termination define how and when a chatbot interaction should pause, redirect, reset, or end. In Aimogen, conversations do not run indefinitely unless you allow them to. You can explicitly decide when the chatbot should stop responding, change behavior, or close a conversation loop.
This is handled through rules, triggers, and workflows, not AI intuition.
What Conversation Control Means #
Conversation control allows you to:
- end a conversation intentionally
- prevent further AI responses
- redirect the user to another path
- reset conversation state
- escalate or hand off control
- enforce hard stop conditions
Without explicit control, chatbots will continue responding indefinitely.
Why Termination Matters #
Uncontrolled conversations can:
- loop endlessly
- waste API usage
- frustrate users
- violate compliance rules
- drift outside intended scope
Termination is not a failure state. It is a designed outcome.
How Conversations Are Ended #
Conversations can be ended through deterministic actions, not AI decisions.
Common termination actions include:
- explicitly ending the conversation
- disabling further input
- showing a final message
- requiring a new conversation to continue
- handing control to another chatbot
- instructing the AI to stop responding
Termination can be soft or hard, depending on configuration.
Soft vs Hard Termination #
Soft Termination #
Soft termination:
- sends a final message
- stops the current flow
- allows the user to start a new conversation manually
Example:
“Thanks for reaching out. If you need anything else, start a new chat.”
This is the most common pattern.
Hard Termination #
Hard termination:
- blocks further input
- prevents AI responses
- requires explicit reset or new session
Used for:
- compliance enforcement
- abusive behavior handling
- end-of-process flows
- data collection completion
Hard termination should be used sparingly.
Trigger-Based Termination #
Conversations can be terminated when:
- a specific keyword is detected
- a workflow reaches its final step
- required information has been collected
- a condition is met (e.g. consent declined)
- usage limits are reached
Triggers define when termination happens. Actions define how.
Termination After Lead Collection #
A common pattern is:
- collect required lead data
- confirm receipt
- terminate the conversation
This prevents:
- repeated data requests
- unnecessary AI usage
- confusion about next steps
Termination ensures clarity.
Escalation Instead of Termination #
In some cases, you don’t want to end the conversation, but change who handles it.
Instead of terminating, you can:
- route to another chatbot
- switch persona
- append stricter system prompts
- move into a different workflow
Termination is not the only control mechanism.
Conversation Reset #
You can reset conversation state by:
- forcing a new conversation
- clearing prior context
- restarting the chatbot flow
This is useful when:
- users get stuck
- context becomes irrelevant
- workflows need a clean start
Reset is different from termination. Reset allows continuation without history.
User-Initiated Termination #
Users may end conversations by:
- closing the chatbot
- starting a new conversation
- navigating away
You can optionally:
- detect restart actions
- send a fresh welcome message
- avoid restoring old context
User intent should be respected.
AI-Level Termination Instructions #
You can instruct the AI to:
- respond once and then stop
- refuse to answer further
- redirect users elsewhere
This is done via appended system prompts, not user messages.
AI termination instructions should always be backed by rule-based logic.
What Conversation Control Does Not Do #
Conversation control does not:
- retrain the AI
- modify models
- override placement rules
- persist across chatbots
- affect frontend rendering
- bypass privacy or consent logic
It operates strictly at conversation runtime.
Common Mistakes #
- never terminating conversations
- relying on AI to “know when to stop”
- overusing hard termination
- terminating without explaining why
- allowing loops after workflow completion
- forgetting to reset state on new conversations
Termination should feel intentional, not abrupt.
Best Practices #
- always explain why the conversation ended
- prefer soft termination where possible
- terminate after completing structured workflows
- use escalation instead of termination when appropriate
- test termination paths thoroughly
- monitor logs for repeated loops
Clear endings improve user trust.
Summary #
Conversation Control & Termination in Aimogen give you explicit authority over when chatbot interactions should end, reset, or change direction. Termination is handled through triggers and workflows, not AI guesswork, ensuring predictable behavior, reduced costs, and better user experience. Well-designed endings are as important as good responses—they signal completion, clarity, and control.
