Agent Readiness Survey
Last updated: October 27, 2026
The AI Agent Readiness survey is a ready-made Span survey that asks engineers how they work with AI coding agents and how ready their team and codebase are for agentic development.
Why it matters
A general developer experience survey doesn't tell you whether your teams are ready to get value from AI agents. This survey surfaces gaps in trust, habits, tooling, and codebase quality before they become blockers.
For engineering leaders: see which teams are ahead and which need support before a major AI tooling investment.
For platform and DevEx teams: find the guardrails, documentation, and test coverage gaps engineers say hold agents back.
How it works
The survey is designed for individual contributors who write or review code: developers, platform engineers, and anyone shipping changes. It works best sent broadly, so you can compare teams and levels. It has 15 questions in two themes: 2 rating questions, 10 multiple-choice questions, and 3 free-text questions.
Personal AI Readiness measures individual confidence, habits, and engagement with AI tools.
Question | Type |
|---|---|
I trust my AI-generated code to be correct and functional. | Rating (Agreement) |
How often do you use AI tools as part of your development workflow? | Multiple choice |
What best describes your role? (e.g. frontend, backend, fullstack, platform/infra, EM) | Free text |
When AI-generated code passes your initial read, what do you typically do next? | Multiple choice |
For tasks you now delegate to AI, how would you describe your ability to do them without it? | Multiple choice |
How has AI changed your ability to deliver work? Select all that apply. | Multiple select |
What are your biggest concerns with AI driven development? | Multiple select |
What it reveals: whether developers review AI output critically, keep their own skills, and trust the code they ship.
Team & Systems Readiness measures infrastructure, process, and organizational maturity for agentic development.
Question | Type |
|---|---|
Our codebase is well prepared for agentic development. | Rating (Agreement) |
When you or a teammate discovers a prompt or AI workflow that works well, what happens next? | Multiple choice |
Which best describes the current state of your code repositories? | Multiple choice |
Which best describes the controls your team has against agent-specific failure modes such as runaway loops, unintended actions, or compounding errors? | Multiple choice |
Are AI agents being used for operational work like monitoring, incident response, or debugging production issues? | Multiple choice |
How has your product and design process changed because of AI? | Multiple choice |
What actions should your company or team take to further improve your agentic development? | Free text |
What is the biggest barrier to adopting more AI in your development workflow? | Free text |
What it reveals: whether the codebase is set up for agents (tests, documentation, clear architecture), whether people share what works, what guardrails exist, and how far AI has changed work outside engineering.
Setup
Open Insights → Sentiment, click Create survey, and choose AI Agent Readiness. You need the Manage Surveys permission. The wizard has five steps:
Settings: name the survey and choose Transparent (names visible, recommended) or Anonymous (results show only after at least 4 people respond).
Questions: turn the template themes and questions on or off and add custom questions. On the same step, you can ask people to vote for the areas most in need of improvement: off, pick 1 to 3, or pick 3.
Participants: choose people by criteria (team, job level, job title, and more) or by hand. You can cap the number with a random sample and skip people surveyed recently.
Communications: send by Email, Slack, Email + Slack, or a Share link. Customize the launch message and up to two reminders, and set a close date (1 to 2 weeks is typical). Share-link surveys send no automated messages.
Confirm: launch now or save as a draft.
Reading results
Results update as people respond, so you can check them while the survey is live. Anonymous surveys stay hidden until enough people respond. AI insights appear once the survey closes.
Summary: responses, participation rate, positive sentiment, and the top voted areas.
Heatmap: results by Teams, Managers, or Job Levels. Transparent surveys can also group by Tenure and Custom Field.
Themes and questions: answer distributions, response counts, and free-text answers.
AI insights: an executive summary with the top 3 and bottom 3 themes, top voted areas, and summarized comment themes.
Things to know
Multi-select and free-text answers show as answer distributions and comments.
Anonymous surveys need at least 4 participants to launch, and slicing by more attributes raises the number of respondents needed.
Tenure and custom-field heatmaps are available for Transparent surveys only.
Results don't wait for close. Only the AI insights card waits until the survey closes.
Worked example
Thirty engineers answer "Our codebase is well prepared for agentic development." Twelve choose 4 or 5, so positive sentiment for that question is 12 / 30 = 40%. On the heatmap by team, a platform team at 75% and a mobile team at 20% tell you where to look first. The free-text answers to "What is the biggest barrier..." usually explain the gap.
Tips
Run it before a major AI tooling investment so you have a baseline to measure change against.
Segment by team. Readiness is rarely uniform.
Read the free-text answers. The barrier and improvement questions often give concrete next steps that scores alone don't.
Share what you'll act on after the survey closes. People respond better to the next survey when they see results.
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Span DX Surveys: Complete Guide
Need help?
Contact your Span account team or reach out to support@span.app.