# Agent Logs in Futuria CRM: Overview, Benefits and Setup > Find out how to use Agent Logs in Futuria CRM to monitor, analyse and optimise the performance of your AI agents. A complete guide. Author: Futuria Support Team · Published: July 15, 2026 · Reading time: 8 min read Source: https://futuriamarketing.com/en/supporto/log-agenti-panoramica/ --- ## Agent Logs overview Agent Logs give you a centralised way to analyse the behaviour of your AI agents across the various features supported by Futuria CRM. By bringing execution activity, conversation context and step-by-step details together in one place, Agent Logs make it easier to understand what happened, identify problems faster and improve agent performance over time. This article explains what Agent Logs are, why they matter and how to use their three-level diagnostic experience to analyse agent activity in Futuria CRM. ## What are Agent Logs? Agent Logs are the centralised Futuria CRM feature for recording and tracking AI agent activity. They are designed to give you a single point of reference for analysing the logs and traces of the supported applications in the Futuria CRM ecosystem, helping you understand what happens behind the scenes during every interaction. This greater visibility lets you move beyond guesswork, resolve problems with confidence and improve agent behaviour with a clearer view of how decisions are made. Agent Logs are currently available for Agent Studio and Voice AI, with support for other AI products on the way. Agent Logs are structured to support a three-level diagnostic experience: - An overall view of agent activity. - A detailed conversation and execution timeline for a specific interaction. - A granular view of individual steps with in-depth technical details for debugging. ## Key benefits of Agent Logs - **Centralised visibility**: Analyse the activity of supported AI agents in one place, instead of relying only on isolated test views. - **Faster troubleshooting**: Move from a general view of activity to a specific conversation and then to individual execution steps to pinpoint where an error occurred. - **Better debugging context**: Compare the agent's reply with the execution timeline to understand how the final response was produced. - **Performance awareness**: Use step-level execution times to spot slow areas that may need attention. - **Greater confidence**: Inspect technical details more closely when resolving issues related to prompts, logic, variables or tool behaviour. ## Global activity overview The global activity overview gives you an overall view of recent agent activity, so you can quickly identify where to focus your attention. This high-level perspective is useful when you want to monitor activity at scale, narrow down a problem or pinpoint a specific interaction to examine more closely. The global activity overview is the first level of the Agent Logs experience. From here, users can review recent executions and begin their investigation. This view is useful for: - Monitoring recent activity across the supported agent experiences. - Narrowing down which execution to analyse. - Pinpointing a specific interaction more quickly. - Analysing conversations more efficiently in high-volume environments. Common elements and actions in this view may include: | Element or Action | Description | | --- | --- | | Date and time | Shows when the interaction took place so you can analyse activity in context. | | Agent name | Identifies which agent handled the interaction. | | Agent ID | Shows the unique ID of the agent associated with the interaction. | | AI product | Indicates which supported AI product the activity belongs to. | | Channel | Shows the channel in which the interaction took place. | | Status | Shows the outcome or current status of the interaction. | | Filter by product | Narrows the results down to a specific AI product. | | Filter by agent name | Focuses on the activity of a specific agent. | | Search by contact | Helps you find the interactions linked to a specific contact. | | Sort results | Reorders the results so you can find the most relevant interactions faster. | ![Global activity overview in Futuria CRM Agent Logs.](https://assets.cdn.filesafe.space/jOFcdS1QIxGsOiKRyQti/media/ffdba197-1f5d-4a61-9411-3feaeaa0ef3e.gif) ## Conversation context and timeline Viewing the conversation and the execution history together helps you understand both the user experience and the logic driving it. This view is useful when you need to answer questions such as why the agent replied in a certain way, where a decision changed or when a tool was involved. The conversation context and timeline is the second level of the Agent Logs experience. Once you have selected a specific interaction, you can analyse the conversation alongside an execution timeline. This view helps you: - Read the interaction in a familiar conversation format. - Compare the agent's reply with the steps that generated it. - Trace the flow from the user's original message through to the final response. The execution timeline is designed to show the ordered path the agent followed during the interaction, giving you a clearer picture of how the conversation progressed behind the scenes. ``` Task checklist in execution timelines In some Agent Studio executions, the timeline includes a checklist that tracks multi-step work. - Tasks use descriptive labels instead of numeric indexes. - The interface indicates which task is active and which ones are complete. - This improves clarity on progress when the agent adds tasks or changes the plan during execution. ``` ![Task checklist in the execution timeline of an AI agent.](https://assets.cdn.filesafe.space/jOFcdS1QIxGsOiKRyQti/media/9e0a57eb-50de-4eaa-8779-4feb1b51c6a7.png) ## Granular step execution Step-level detail is where troubleshooting becomes far more precise. When a conversation looks wrong or incomplete, inspecting a single execution step can help reveal whether the problem stems from the logic, the timing, data handling or a tool-related action. Depending on the step, users can also analyse the output in different formats, including a raw JSON-style view or a more readable parsed view. For deeper debugging and for sharing, the step output can also be copied as JSON. Granular step execution is the third level of the Agent Logs experience. By selecting a step from the execution timeline, you can examine more detailed information about that part of the interaction. This in-depth view is useful for examining: - The model used for the step. - Latency and execution times. - Date and time details. - Input and output for the selected step. - Prompt details, where applicable. - Additional technical metadata that supports debugging. ``` You may also see structured task details for multi-step executions (for example, labelled tasks and explicit transitions). Use these details to confirm the order of tasks, their completion status and where progress changed. ``` ![Detail of a single step in the execution of an AI agent.](https://assets.cdn.filesafe.space/jOFcdS1QIxGsOiKRyQti/media/a05529dd-6441-4c2e-b1b8-d001cb800e92.gif) ## How to use Agent Logs Agent Logs do not require a separate installation. The most important part of the setup is making sure you have supported agent activity to analyse and that you know how to move from the general view to the detailed execution views. A clear setup process helps you get value from Agent Logs faster and makes troubleshooting more consistent. Follow the steps below to start working with Agent Logs: 1. Open **AI Agents > Agent Logs** in your Futuria CRM account. ![How to reach Agent Logs from the Futuria CRM menu.](https://assets.cdn.filesafe.space/jOFcdS1QIxGsOiKRyQti/media/ae72c6ab-cd5c-4641-995a-7c81822ecf0d.png) 2. Analyse the activity list and choose the interaction you want to inspect. Use filters such as **Agent Name**, **AI Product**, **Channel** or **Status** to narrow the results. ![Filtering the activity list in Agent Logs.](https://assets.cdn.filesafe.space/jOFcdS1QIxGsOiKRyQti/media/c77eb18f-ded7-4317-bd6a-212b09b9cfbe.gif) 3. Open the interaction to view the conversation and the execution timeline. ![Viewing the conversation and the execution timeline.](https://assets.cdn.filesafe.space/jOFcdS1QIxGsOiKRyQti/media/3e430ca3-d2b3-41bc-bd48-69ea559e1d64.png) 4. Select a message or a step to see more detailed execution information. Use these details to improve your agent. ![Inspecting a step to view the execution details.](https://assets.cdn.filesafe.space/jOFcdS1QIxGsOiKRyQti/media/f5bf2f95-d653-4e6b-a22e-b14bd2213ad8.gif) 5. Run the test again and analyse the updated activity in **Agent Logs**. ## Metrics tab (dashboard view) The Metrics tab summarises Agent Logs data in a customisable dashboard. Use it to monitor volumes, performance and operational trends across agents, instead of analysing a single session. ![Metrics tab dashboard in Agent Logs.](https://assets.cdn.filesafe.space/jOFcdS1QIxGsOiKRyQti/media/66f3e3a5-23e3-4b7b-8371-c73d59e47557.png) ### Customise the dashboard layout Select **Edit Layout** to change the look of the dashboard: - Add or remove widgets. - Drag and drop widgets to rearrange the layout. - Resize widgets to focus on specific charts or tables. - Save several layouts and switch between them using the layout drop-down menu. ![Customising the layout of the metrics dashboard.](https://assets.cdn.filesafe.space/jOFcdS1QIxGsOiKRyQti/media/eeb8ce45-8dbe-4fe1-a908-594798b4632c.gif) ### What the Metrics tab helps you track The metrics widgets help you monitor: - **Key KPIs** (for example, conversations handled, AI messages and average messages per conversation). - **Performance** (for example, average response time and top-performing agents). - **Operational status** (for example, top actions and average execution latency). - **Trends** (for example, peak hours and channel activity over time). ![Examples of the widgets available in the AI agents metrics dashboard.](https://assets.cdn.filesafe.space/jOFcdS1QIxGsOiKRyQti/media/a7e0a3e5-e105-49ff-a7cb-cec9f5427a1e.gif) For full widget definitions and dashboard configuration examples, see the Agent Logs Metrics guide. ## Frequently Asked Questions ##### What are Agent Logs mainly used for? Agent Logs help you understand how an AI agent handled an interaction, combining conversation context with execution details in a centralised view. ##### Are Agent Logs the same thing as the Message Execution Timeline in Agent Studio? No. Agent Logs are a centralised recording and tracking feature, whereas the Message Execution Timeline is part of the Agent Studio testing and debugging experience. ##### When should I use Agent Logs? Use Agent Logs when you want to investigate how an agent replied, analyse the path of an interaction or resolve a problem at a specific step of the execution flow. ##### Can Agent Logs help resolve issues with slow or complex interactions? Yes. The step-level view is designed to provide a more detailed picture of execution, including the timings and technical context that can help with troubleshooting. ##### Will Agent Logs support other AI products in the future? Yes. At launch, Agent Logs are available for Agent Studio, with support for further AI products on the way.