> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sketricgen.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Traces & Optimization
> Inspect agent reasoning, tool calls, handoffs, latency, and credit consumption with the Trace Explorer — debug and optimize your AI workflows.
## Overview
Traces give you a transparent view into how your agents think, act, and collaborate inside the **AgentSpace**.
They show **what happens under the hood** during a workflow run, which agents were called, what tools they used, how long each step took, and how many credits were consumed.
The **Trace Explorer** is built directly into the AgentSpace and Playground so you can debug and optimize your workflows in real time.
## Viewing Traces in the Playground
When testing your workflow in the Playground, each message execution is automatically logged with a corresponding **trace record**.
You can open and explore these traces to see exactly how your agents responded to user input.
Each trace provides:
* The **entry agent** that first received the message.
* Any **handoffs** made between agents.
* Detailed **function calls** (tool invocations or agent transfers).
* The **time taken** and **credits consumed** per step.
* A complete breakdown of each agent’s reasoning and outputs.
You can expand each agent’s trace to inspect:
* The **function arguments** or structured data passed downstream.
* The **structured outputs** produced.
* The exact **handoff path** followed during orchestration.
This makes the Playground the ideal environment for iterative debugging — test, observe, refine, and retest within seconds.
## Trace Explorer in the AgentSpace
Within the AgentSpace, traces are tied to workflow executions and can be accessed after running a test.
When a workflow is executed:
* Each **tool call**, **handoff**, and **agent decision** is captured as a trace event.
* You can open the **Trace Explorer** to view the complete sequence of actions.
* Every trace displays execution metrics — including agent runtime, tool latency, and credit cost.
The Trace Explorer provides a structured timeline of events, showing how your multi-agent system reached its final output.
**Key insights include:**
* Which agent initiated and completed each step.
* The order and duration of handoffs.
* How structured inputs and outputs flowed between nodes.
* Any failed or retried tool calls.
## Optimization Quick Wins
Once you’ve reviewed your traces, you can apply these best practices to improve performance and reliability.
* Tighten **agent instructions** — define when to call each tool and what success looks like.
* Use **Agent-as-Tool** for recurring, contained subtasks (classification, summarization, extraction).
* Prefer **Structured Outputs** to avoid parsing errors and reduce retry loops.
* The system automatically retries tool calls on failure (once or twice), but there is no user-facing retry or fallback configuration.
* Keep sensitive credentials secure; rotate API keys periodically.
* Review Trace Explorer regularly to identify latency sources or redundant operations.
* Simplify orchestration paths — fewer handoffs improve both speed and cost efficiency.
**Guideline:**
Think of traces as your workflow’s feedback loop. Continuous monitoring and small refinements after each trace review lead to dramatically better agent performance.