> ## Documentation Index > Fetch the complete documentation index at: https://docs.galtea.ai/llms.txt > Use this file to discover all available pages before exploring further. # How to evaluate your agentic system? > A practical guide for evaluating your agentic system: use Galtea's tracing feature to capture your agent's traces and then define your specific scope of interest by creating a custom metric. Use the [`@trace` decorator](/sdk/tutorials/tracing-agent-operations) to capture every operation your agent performs. Once captured, traces become an evaluation parameter called `traces` — the full execution path from user input to system output. Galtea provides built-in metrics like [Tool Correctness](/concepts/metric/tool-correctness), but agentic systems are often highly specific. We recommend creating [custom judge metrics](/sdk/tutorials/how-to-create-your-llm-as-a-judge-prompt) that check the exact behaviors you care about. Below are practical examples to get you started. ### Example 1: Was Tool X called before Tool Y *Example of Judge Prompt:* ``` **Evaluation Criteria:** Check if the TRACES reflect correct agent behavior by comparing them to what was expected. Focus on: 1. Whether Tool X is invoked at least once in the traces 2. Whether Tool Y is invoked only after Tool X 3. Whether the ordering of tool calls follows the intended system logic **Rubric:** Score 1 (Good): Tool X is called before Tool Y, and Tool Y is never called first in the traces. Score 0 (Bad): Tool Y is called before Tool X, or Tool X is never called. ``` ### Example 2: Were all attributes collected before calling Tool X? *Example of Judge Prompt:* ``` **Evaluation Criteria:** Check if the TRACES reflect correct agent behavior by comparing them to what was expected. Focus on: 1. Whether attribute_a, attribute_b, and attribute_c appear in the traces before Tool X is called 2. Whether these attributes are explicitly present in the traces 3. Whether the attributes are derived from user input or tool outputs rather than invented **Rubric:** Score 1 (Good): All required attributes are explicitly present and correctly sourced before Tool X is invoked. Score 0 (Bad): Any attribute is missing, implicit, or invented before calling Tool X. ``` ### Example 3: Do attributes come from the correct sources? *Example of Judge Prompt:* ``` **Evaluation Criteria:** Check if the TRACES reflect correct agent behavior by comparing them to what was expected. Focus on: 1. Whether attribute_b originates from Tool Y output rather than user input 2. Whether attribute_c is recomputed after Tool Z when Tool Z is called 3. Whether attributes are overridden only when justified by a tool output **Rubric:** Score 1 (Good): Attributes in the traces originate from the correct tools and are updated appropriately. Score 0 (Bad): Any attribute is sourced incorrectly, reused improperly, or overridden without justification. ``` ### Example 4: Are tools used under required conditions? *Example of Judge Prompt:* ``` **Evaluation Criteria:** Check if the TRACES reflect correct agent behavior by comparing them to what was expected. Focus on: 1. Whether Tool Z is called only when attribute_a exceeds the required threshold 2. Whether Tool X is skipped when attribute_c is false 3. Whether conditional branching in the traces matches tool results **Rubric:** Score 1 (Good): Tools are invoked or skipped strictly according to the required conditions. Score 0 (Bad): Any tool is called or skipped in violation of the defined conditions. ``` ### Example 5: Are the tool outputs actually used? Ensure tool outputs are actually *used*. *Example of Judge Prompt:* ``` **Evaluation Criteria:** Check if the TRACES reflect correct agent behavior by comparing them to what was expected. Focus on: 1. Whether field_d from Tool X output is referenced later in the traces 2. Whether Tool Y output influences subsequent reasoning or tool calls 3. Whether tool outputs are respected and not contradicted later in the traces **Rubric:** Score 1 (Good): Tool outputs are explicitly used and consistently reflected in later steps. Score 0 (Bad): Tool outputs are ignored, unused, or contradicted. ``` ## Next Steps Capture traces from your agent — required for these metrics to work. Write and refine custom judge prompts for any evaluation criteria.