The Opportunity

Mindbody is seeking a Principal Product Manager, Agentic Experience to define how AI transforms the way businesses use our industry-leading B2B software platform.

This role will own the vision, strategy, and execution for incorporating agentic workflows into Mindbody’s business experience. Your mandate spans both sides of the opportunity: envisioning an AI-first user experience that meaningfully improves how customers operate their businesses, and establishing the underlying platform capabilities that allow Mindbody teams to build reliable agentic workflows quickly, safely, and at scale.

This is not a role focused on adding isolated AI features or conversational interfaces to existing workflows. You will determine where agents can fundamentally simplify work, coordinate actions across the platform, and help customers achieve outcomes with less effort. You will also define the quality, evaluation, trust, and governance systems necessary to make non-deterministic products dependable in real business environments.

As a Principal Product Manager, you will operate as a senior individual contributor and organizational leader. You will level up Mindbody’s thinking about applied AI, align teams around a cohesive strategy, and create the platform and product patterns that shape one of the most important components of Mindbody’s next evolution.

The Role You’ll Play

Define Mindbody’s agentic product strategy

• Establish a compelling vision for an AI-first B2B user experience across Mindbody’s platform.
• Develop and own the product strategy and roadmap for agentic experiences and their enabling platform capabilities.
• Identify the workflows where agentic systems can create meaningful customer and business value—not merely add novelty or incremental convenience.
• Determine the appropriate interaction models for agents, including proactive assistance, workflow orchestration, recommendations, approvals, and autonomous actions.
• Translate rapidly evolving AI capabilities into durable product strategy grounded in customer needs, technical feasibility, economics, and risk.
• Define clear boundaries between deterministic software, AI-assisted experiences, and agent-led workflows.

Build trusted agentic experiences

• Lead the development of agentic workflows designed specifically for B2B users and their operational needs.
• Create experiences that make agent reasoning, proposed actions, uncertainty, and outcomes understandable to users.
• Define when agents should act autonomously, request approval, escalate to a person, or decline to act.
• Ensure agentic workflows respect permissions, business rules, data boundaries, and customer-specific context.
• Design for user trust and control through confirmation patterns, reversibility, auditability, and effective recovery from errors.
• Partner with Design and Research to establish reusable interaction patterns for AI-first experiences across Mindbody.

Establish the enabling AI platform

• Define the product requirements for a shared platform that enables teams to create, test, deploy, observe, and improve agentic workflows efficiently.
• Partner deeply with Engineering, Architecture, Data, Security, and Privacy to develop scalable capabilities for model access, orchestration, tool use, context management, permissions, observability, and governance.
• Establish reusable primitives and product patterns that accelerate delivery while maintaining consistent standards for quality and safety.
• Make thoughtful build, buy, and partner decisions in a fast-moving technology ecosystem.
• Define an adoption strategy that makes the platform valuable and practical for product and engineering teams across Mindbody.
• Balance experimentation with the architectural discipline required to support production-grade B2B software at scale.

Create rigorous evaluation and quality systems

• Define quality standards for non-deterministic products and establish how those standards will be measured.
• Build an evaluation strategy spanning offline evaluations, production monitoring, human review, experimentation, and customer feedback.
• Develop representative test sets and evaluation criteria for task completion, correctness, groundedness, tool selection, policy adherence, latency, cost, and user trust.
• Establish release criteria and ongoing monitoring for workflows whose behavior cannot be validated through traditional deterministic testing alone.
• Create feedback loops that convert production signals and failure modes into measurable product improvements.
• Partner with technical teams to address hallucinations, model drift, prompt and workflow regressions, ambiguous intent, and failures across multi-step tasks.

Lead across the organization

• Serve as a company-wide thought leader on agentic products and applied AI.
• Align executives, product leaders, and technical teams around a shared vision, investment strategy, and operating model.
• Influence roadmaps across organizational boundaries and drive adoption of shared AI capabilities and standards.
• Coach product teams on agentic product discovery, workflow selection, evaluation, and responsible delivery.
• Communicate complex technical concepts, uncertainties, and tradeoffs clearly to technical and nontechnical audiences.
• Engage directly with customers to understand their workflows, trust thresholds, and readiness for different levels of AI assistance and autonomy.
• Create momentum while maintaining a high bar for customer value, reliability, security, and responsible use.

The Experience You Bring

• 9-12 years of product management experience, including strategic leadership of complex platform or cross-product initiatives.
• Hands-on product leadership experience taking LLM-powered or agentic systems from concept to production.
• Experience designing agentic user experiences or workflows for B2B users, including workflows that reason over context, use tools, coordinate multiple steps, or take actions on a user’s behalf.
• Deep understanding of the distinct product challenges posed by non-deterministic systems.
• Demonstrated experience establishing evaluation and quality systems for production AI products, including offline and online evaluation methods.
• Strong working knowledge of modern agentic system concepts such as orchestration, retrieval, tool calling, context management, structured outputs, guardrails, observability, and human-in-the-loop controls.
• Experience building platform capabilities used by multiple product and engineering teams.
• A track record of driving platform adoption and influencing roadmaps across teams without relying on direct authority.
• Significant B2B SaaS experience and an understanding of the reliability, permissions, security, auditability, and change-management expectations of business customers.
• Ability to connect technical platform decisions to user experience, product outcomes, and business strategy.
• Strong judgment regarding the appropriate balance among autonomy, user control, reliability, speed, cost, and risk.
• Executive-level communication skills and the ability to create clarity in a technically complex, rapidly changing space.

What Will Set You Apart

• You have launched and scaled production agents that take consequential actions inside complex B2B workflows—not only copilots, chat interfaces, or content-generation features.
• You have owned an AI platform, agent development framework, or shared set of capabilities adopted by multiple product teams.
• You have developed evaluation systems that meaningfully predicted production quality and informed release decisions.
• You understand how to evaluate end-to-end agent performance, including planning, tool selection, task completion, policy compliance, and recovery from failure.
• You have designed AI experiences for users operating complex businesses, where mistakes can affect customers, schedules, communications, payments, or revenue.
• You have evolved an established B2B SaaS product toward an AI-first experience while respecting existing workflows, customer trust, and platform constraints.
• You can distinguish between compelling demonstrations and durable products—and know what it takes to close the gap between them.
• You remain current on the AI ecosystem while avoiding strategy driven by hype, individual models, or short-lived implementation patterns. 

Pay Transparency

It is Playlist's intent to pay all Team Members competitive wages and salaries that are motivational, fair and equitable. The goal of Playlist's compensation program is to be transparent, attract potential employees, meet the needs of all current employees, and encourage Team Members to stay with our organization.

Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location.

The base salary range for this position in the United States is $180,000 to $242,000. The total compensation package for this position may also include performance bonus, benefits and/or other applicable incentive compensation plans.