An AI agent records and explains its decision chain through a combination of logging, reasoning traces, and structured outputs. Here's how it works and an example:
Logging Decisions: The agent logs each step it takes, including input data, intermediate conclusions, and final outputs. This creates an audit trail for transparency.
Example: In a medical diagnosis AI, the agent logs patient symptoms, considered conditions, and confidence scores for each diagnosis.
Reasoning Traces: The agent provides step-by-step explanations of how it arrived at a decision, often using logic or probabilistic models.
Example: A financial risk assessment AI might explain that it flagged a transaction as fraudulent due to unusual spending patterns, location mismatches, and historical data.
Structured Outputs: The decision chain is formatted in a way that’s easy to interpret, such as decision trees, rule sets, or natural language summaries.
Example: A customer support chatbot might break down its response by listing the detected issue, referenced policies, and suggested solutions.
For cloud-based implementations, Tencent Cloud offers services like TI-ONE (AI Platform) and Cloud Log Service (CLS) to store, analyze, and visualize AI decision logs. These tools help monitor and explain AI behavior at scale.
Additionally, Tencent Cloud’s AI Model Management features enable version control and traceability for AI models, ensuring decisions can be audited and improved over time.