> ## Documentation Index > Fetch the complete documentation index at: https://docs.dacard.ai/llms.txt > Use this file to discover all available pages before exploring further. # Agent Studio > Configure and monitor autonomous agents that take moves to improve your product maturity. Agent Studio is the surface for managing autonomous workflows. Agents run on schedule or trigger, analyze your product data, and either surface findings or dispatch moves into Linear, Slack, or the re-score queue. Pro plans and above. # Agent Studio Agent Studio is the surface for managing autonomous agents. Agents run on schedule or event triggers, analyze your product data, and produce structured artifacts or dispatch moves to connected tools (Linear, Slack, re-score queue). Agent Studio is available on **Pro plans and above**. ## What agents do Agents are purpose-built workflows that monitor specific aspects of your product maturity: Reads scoring trajectory, surfaces cross-product patterns, and surfaces strategic moves for your portfolio. Monitors score moves and adapter signal for unusual patterns, alerting you to regressions or unexpected lifts. Tracks competitor product changes and scoring moves, surfacing competitive intelligence in your dashboard. Surfaces periodic coaching summaries grounded in your scores, highlighting the highest-impact moves for your current stage. Monitors your lifecycle placement and flags when team behavior diverges from expected patterns for your stage. Closes the feedback loop by linking dispatched moves to measured score lifts over time. ## Getting started Open **Agents** from the main navigation. Your account will be provisioned with default agent definitions on first visit. Each agent shows its type, description, current status (active or paused), and last run time. Default agents start at Notify autonomy. Toggle an agent from **Paused** to **Active** to start its scheduled runs. After validating an agent's output quality over several runs, promote it to Suggest or Auto to enable action dispatch. ## Autonomy levels Every agent has a configurable autonomy level that controls how much it can do without human approval: | Level | Behavior | Best for | | ----------- | --------------------------------------------------------------------------------------- | -------------------------------------- | | **Notify** | Surfaces findings in the dashboard only. No automated actions. | New agents you are still evaluating | | **Suggest** | Creates draft actions that go into your **approval queue** for review before execution. | Agents you trust but want to oversee | | **Auto** | Executes configured actions immediately when conditions are met. | High-confidence, well-validated agents | ### Approval queue (Suggest mode) When an agent is set to **Suggest**, its actions are queued for human review instead of executing immediately. Each pending action shows: * The agent that produced it and the run that triggered it * The proposed action (Linear issue title, description, priority) * An **Approve** or **Reject** button with an optional reason field Approved actions execute immediately. Rejected actions are recorded in the decision history so the system learns from your judgment over time. New agents default to **Notify**. Only promote to Auto after validating output quality over several runs. ### Review before dispatch Agents with **Review required** enabled hold their artifacts (strategic briefs, anomaly reports) for human review before dispatching to Slack or email. Enable this under **Settings > Review** on any agent to ensure a human sees every insight before it reaches the team. Set the autonomy level from the agent configuration panel under **Settings > Autonomy**. ## Configuring triggers Each agent can be triggered in multiple ways: | Trigger type | Description | | ------------ | ---------------------------------------------------------------------------------------- | | **Schedule** | Runs on a recurring schedule (daily, weekly, or custom cron expression) | | **Event** | Fires when a specific condition is met (score threshold, integration sync, delta breach) | | **Manual** | Run on demand from the Agent Studio UI | ### Trigger conditions Event-based triggers support conditions that refine when they fire: * **Score threshold** trigger when a dimension score drops below a specified value * **Score delta** trigger when a composite score changes by more than N points * **Sync completed** trigger after a specific integration syncs new data * **Product added** trigger when a new product is created in the account Each trigger has a configurable **cooldown** (minimum time between firings) to prevent noise. Start with the default weekly schedule. Once you are comfortable with the agent's output, add event-based triggers for real-time alerts. ## Actions Agents set to **Suggest** or **Auto** autonomy can dispatch real actions when triggered: | Action type | What it does | | --------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Create Linear issue** | Opens a Linear issue with the finding, suggested owner, and priority | | **Send Slack message** | Posts a structured summary to a configured channel | | **Trigger re-score** | Queues a fresh score of the affected product | | **Queue coaching recommendation** | Surfaces a "Do This Next" item in the next DAC session | | **Refresh documentation** | Creates a Linear ticket flagging documentation that has drifted from implementation. Auto-generated by the Spec Quality agent when dimensions score 2 or below. | All dispatched actions are logged in the agent run history with a full audit trail. In Suggest mode, actions appear as drafts in your approval queue before being sent. ## Compound flywheel The compound flywheel tracks whether agent actions are producing measurable score improvements over time. For each dispatched action: | Field | Description | | ------------------ | ----------------------------------------------------- | | **Linked outcome** | Which dimension the action targeted | | **Before score** | Dimension score at the time the action was dispatched | | **After score** | Dimension score on the next re-score | | **Loop closed** | Whether the improvement has been verified | ### Learning cycles The flywheel counts completed **learning cycles** (Score, Connect, Correlate, Act, Learn). Each complete revolution makes the next revolution more accurate. The dashboard shows your cycle count, accuracy trend over time, and intelligence depth percentage. ### Outcome attribution When an action closes its loop (score improves), you can annotate **why** the score changed: | Attribution | Meaning | | --------------------------- | ---------------------------------------------------------- | | **Recommendation followed** | The team implemented the recommendation as suggested | | **External factor** | Score changed due to hiring, reorg, or market shift | | **Coincidence** | No causal relationship between the action and outcome | | **Partial** | The recommendation was one of several contributing factors | These annotations feed back into the intelligence engine so future recommendations become more accurate. Access the flywheel from the Agent Studio dashboard or from any agent's detail page. ## Product vitals Product vitals surfaces real-time operational health signals for your active products. The vitals panel appears on the Intelligence dashboard and updates on every agent sync: | Metric | What it measures | | ---------------------- | ----------------------------------------------------- | | **Agent runs** | Total agent executions in the current period | | **Insights generated** | Distinct findings surfaced across all agent runs | | **Actions dispatched** | Actions sent to Linear, Slack, re-score queue, etc. | | **Loops closed** | Actions verified to have improved a score | | **Score velocity** | Composite score change per week over the last 30 days | ## LLM-grounded narratives Strategic Intelligence and Anomaly Detection agents use Claude to surface human-quality narratives grounded in your scoring data. The agent engine runs a structured analysis first (identifying patterns, anomalies, and priorities), then enriches the output with an LLM call that produces readable strategic briefs with cited evidence. Other agents (Voice of Customer, Strategy Brief, Spec Quality, Code Quality) run as pure heuristic analyzers by default. You can enable LLM narratives for any agent under **Settings > Model** by selecting a model. LLM-grounded runs consume more credits than heuristic runs but produce richer narratives. Start with Haiku for cost efficiency. ## Browsing artifacts Every agent run produces one or more **artifacts**, structured outputs stored in your account: * **Reports** Markdown-formatted analysis with data and recommendations * **Alerts** Short notifications about anomalies or threshold breaches * **Recommendations** Specific "Do This Next" actions ranked by impact Navigate to the agent detail page and select the **Runs** tab. Click any artifact to view its full content. Artifacts are stored permanently and can be referenced in DAC coaching conversations. ## Performance monitoring | Metric | Description | | ----------------------- | ------------------------------------------------ | | **Run history** | Timeline of all agent executions with status | | **Success rate** | Percentage of runs that completed without errors | | **Average duration** | Typical run time for planning capacity | | **Artifacts generated** | Count of outputs produced per run | | **Token usage** | LLM tokens consumed per run for cost tracking | ### Troubleshooting failed runs 1. Check the run detail page for error messages 2. Verify that connected integrations are still authorized 3. Confirm the product being analyzed still exists 4. Review your credit balance (agent runs consume credits) Agent runs consume credits. A typical strategic intelligence run uses 1-3 credits. Monitor usage under **Settings > Limits**. ## Managing agents | Action | Description | | ---------- | ------------------------------------------------------------------------------------ | | **Pause** | Stop scheduled runs without losing configuration | | **Resume** | Re-enable a paused agent | | **Edit** | Change the agent's name, description, or configuration | | **Delete** | Permanently remove the agent and its trigger configurations (artifacts are retained) | ## Next steps Agents work best with rich signal from connected tools. Reference agent artifacts in coaching for deeper analysis. A guide to configuring agents as a team lead or VP. Manage agents programmatically via the REST API.