> ## Documentation Index > Fetch the complete documentation index at: https://docs.withgauge.com/llms.txt > Use this file to discover all available pages before exploring further. # Agent Preference > Measure which products coding agents choose, recommend, and adopt for real developer tasks. Agent Preference measures selection. It tells you which product a coding agent chooses when it needs to solve a developer problem, why it made that choice, and whether it followed through in code. The central question is: > When an agent has a choice, does it choose you? This is different from asking whether an agent can use your product successfully. [Agent Experience](/agents/concepts/agent-experience) measures implementation success after the task and pass criteria are defined. ## What Agent Preference reveals An Agent Preference run can show whether an agent: * Includes your product among the options it considers * Ranks your product as its first choice * Chooses a competitor instead * Recommends one product but installs another * Mentions several options without making a clear choice * Uses documentation or third-party sources while deciding Gauge bases the result on the complete session. It reads the agent's trace, research activity, and final working-tree diff. The diff helps distinguish a product the agent mentioned from one it actually adopted. ## Measurement types Ask an open-ended developer question without naming a product. The agent chooses freely, and Gauge records its ranked picks and whether each pick was adopted. Name two products and ask the agent to choose between them. Gauge records a winner or `neither`, with reasoning for both products. ### Recommendation prompts A recommendation prompt measures unaided preference when it does not name your product or a competitor. ```text theme={null} Add authentication to this Next.js application. Choose an authentication provider. Implement sign-in. Protect the account page. ``` For each run, Gauge records: * Ranked product picks * The top recommendation * Whether each product was actually adopted * Whether the agent converged on one clear choice * Evidence-backed reasoning for each rank If a recommendation prompt names a product, mark it as branded. Branded runs remain useful for qualitative evidence, but Gauge excludes them from the organic ranking because the prompt influenced the choice. ### Head-to-head prompts A head-to-head prompt measures a direct tradeoff between two named products. ```text theme={null} Add authentication to this Next.js application. Compare Clerk and Auth0. Choose one provider. Complete the integration. ``` Gauge records one of three outcomes: * Product A won * Product B won * Neither won because the agent used both, chose a third option, or did not reach a clear decision Head-to-head results are always branded. Use them to understand a specific competitive decision, not to estimate unaided market preference. ## How a preference prompt works Describe a task the way a developer would ask a coding agent. Keep the task concrete enough that the agent must choose and use a solution. A market groups prompts that measure the same capability, such as authentication, databases, or email delivery. Market scope keeps rankings and findings comparable. Run presets define the repositories, agents, models, personas, skills, and MCP servers used for the measurement. Every attached preset runs during the cycle. Use the shared Agent Preference cycle for recurring measurement, or run a prompt immediately when you need fresh evidence. Gauge inspects the trace and diff to identify the chosen product, the alternatives, the final adoption state, and the evidence behind the decision. ## Read the results Gauge keeps different measurements separate because they answer different questions. | Result | What it means | | ------------------- | ------------------------------------------------------------------------------- | | Organic chosen rate | How often a brand was the top choice in eligible, unbranded recommendation runs | | Ranked picks | The products the agent recommended, in order | | Adopted | The agent wired or installed the product in the final code | | Head-to-head record | Counts of wins, losses, and neither outcomes for a specific pair | | Cited domains | Sources agents consulted while deciding | | Reasoning | Run-level evidence explaining the choice | Do not combine organic chosen rates and head-to-head outcomes into one score. Their prompts and denominators differ. ## Design useful prompts For an organic recommendation: * Write one to three sentences in a developer's voice. * Describe the capability and desired result. * Do not name your product, package names, or competitors. * Give the agent enough room to choose a solution. For a head-to-head: * Name both products in the prompt. * Ask the agent to make and implement one choice. * Use the same task and run setup when comparing results over time. Start most preference prompts in a greenfield repository. Use an existing repository only when its framework, dependencies, or architecture are part of the decision you want to measure. ## Interpret the evidence Agent Preference results describe the runs you sampled. They are not a census of every agent or developer task. * Report counts alongside rates. * Compare results within the same market and prompt type. * Keep run presets stable when measuring change over time. * Separate being mentioned from being ranked first or adopted. * Read the run reasoning before turning a result into an action. Repeated evidence can point to changes you control, such as clearer positioning, better ecosystem documentation, or a working starter template. Measure whether agents can complete a task with your product after they choose or are directed to use it.