Chat History
All messages generated by AI Agents are logged and available for review under Chat History. This feature provides complete visibility into user conversations, execution traces, and decision-making logic used by the Agent. It is designed for debugging, training optimization, and behavior auditing.
Each conversation entry displays:
User and Agent messages
Message timestamps
Channel source (e.g., WhatsApp, Widget, API)
Credit usage (if applicable)
Message Inspection
Selecting any Agent message opens the Inspection Panel, a technical trace viewer that exposes the full reasoning path of the AI model. This panel displays:
Model Used
The LLM responsible for generating the response (e.g., Claude Sonnet 4.5, GPT-4o).
Execution Time
Duration in seconds for message completion.
Reasoning Steps
Internal thinking layers, including Planning, Task Selection, and Tool Calls.
Inputs and Outputs
Structured JSON view of the input data, LLM prompts, and resulting output.
Credits Used
Platform credit consumption for the operation.
The Inspection view helps technical teams trace why the Agent responded a certain way — mapping each reasoning phase from prompt parsing → tool usage → final output.
Note: This feature is critical for understanding hallucinations, context loss, or suboptimal responses and allows for precise prompt refinement.
Agent Optimization
By studying conversation logs and inspections, developers and operations teams can identify:
Frequent escalation patterns (topics leading to human handoffs).
Long reasoning loops or high credit usage per task.
Prompt sections that cause redundant or low-quality outputs.
Data from Chat History can be used to:
Rewrite system prompts.
Adjust model temperature and effort parameters.
Improve Task logic and condition definitions.
Train Agents on realistic interaction data.
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