Agent Readiness Survey
Last updated: May 1, 2026
Overview
The AI Agent Readiness Survey is a structured assessment in Span that helps engineering leaders understand how prepared their teams are to work effectively with AI coding agents. It captures both individual developer mindsets and team-level infrastructure maturity, giving you a complete picture of where your organization stands today — and what needs to change to benefit from agentic development.
Unlike a general developer experience survey, the Agent Readiness Survey is purpose-built for the shift toward AI-assisted and AI-driven workflows. It surfaces gaps in trust, habits, tooling, and codebase quality before they become blockers.
Who Should Take It
The Agent Readiness Survey is designed for individual contributors on engineering teams — developers, platform engineers, and anyone who writes or reviews code. It is most valuable when distributed broadly across the engineering org so you can identify variation across teams, tenures, and roles.
Survey Themes & Questions
The survey is organized into two themes:
🧑💻 Personal AI Readiness
This theme measures individual developer confidence, habits, and cognitive 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? | Open 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? | Multiple select |
What are your biggest concerns with AI-driven development? | Multiple select |
What it reveals: Whether developers are actively engaging with AI output, maintaining their own skills, and developing genuine confidence — or passively accepting suggestions without critical review.
🏗 Team & Systems Readiness
This theme measures infrastructure, process, and organizational maturity for agentic development.
Question | Type |
|---|---|
Our codebase is well prepared for agentic development. | Rating (Agreement) |
When 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? | Multiple choice |
Are AI agents being used for operational work like monitoring, incident response, or debugging? | 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? | Open text |
What is the biggest barrier to adopting more AI in your development workflow? | Open text |
What it reveals: Whether the codebase is structured for agent success (test coverage, documentation, clear architecture), whether knowledge is being shared across the team, what guardrails exist against agent failures, and how deeply AI has changed cross-functional workflows.
Creating and Launching a Survey
Navigate to Surveys in the Span sidebar and click Create Survey. Select AI Agent Readiness as the survey type. The creation wizard walks you through six steps:
Settings — Name your survey and choose an anonymity level (Non-Anonymous or Confidential).
Questions & Themes — Review and toggle on the predefined themes and questions. You can also add custom questions.
Priority Voting — Optionally ask respondents to vote on 1–3 areas most in need of improvement. This surfaces ranked priorities in the results.
Participants — Select participants by team, org structure, or individually. Use smart exclusions to avoid survey fatigue by skipping recently surveyed people.
Communications — Customize the launch email and optional reminders. Set a close date (1–2 weeks is typical). Surveys are distributed via email and Slack.
Confirm & Launch — Review everything and launch immediately, or save as a draft.
Analyzing Results
After the survey closes, results are available in Span under the survey's detail view. The results are broken into four sections:
Summary
High-level completion stats, overall sentiment breakdown (positive / neutral / negative), and the top priority areas voted on by respondents.
Heatmap
A cross-tabulated view of results segmented by team, manager, job level, tenure, or custom fields. This is where you identify which pockets of the org are most and least ready — and whether gaps are team-specific or systemic.
Questions
Question-by-question sentiment distribution and response counts, with open-text responses and comments surfaced per theme.
AI Insights
An AI-generated executive summary including:
The top 3 themes with the highest positive sentiment
The top 3 themes with the lowest sentiment
Top voted priority areas
Summarized comment themes (positive observations, concerns, and suggestions)
Tips for Getting the Most Value
Run it before a major AI tooling investment. Baseline results help you measure change over time.
Segment by team. Agent readiness is rarely uniform — the heatmap will reveal which teams are ahead and which need support.
Use open-text questions. The questions asking about barriers and improvement suggestions often surface concrete, actionable signals that scores alone don't capture.
Follow up with action. Share top-level findings with the engineering org after closing the survey. Teams respond better to future surveys when they see prior feedback acted on.