Lead Product Manager, Agentic AI

What matters most

• Turn opportunities into a launched product

  Own the journey from identifying tax-workflow opportunities through validated prototypes, evaluation performance and production launch. The posting measures success by customer adoption, task-completion accuracy and business impact, not document output.

• Make prototypes and evaluations the specification

  Deliver high-fidelity agent prototypes and, with engineering, evaluation suites that define correctness. The framework must measure accuracy and catch hallucinations and logic failures before they reach a client return.

• Orchestrate agents around existing tax engines

  Translate ambiguous tax tasks and tax SMEs’ judgment into agent workflows, tool contracts, state management and human decision points. Use existing calculation engines for deterministic logic rather than recreating that logic in prompts.

• Make uncertainty visible and recoverable

  Design predictable routes to calculation-engine rules or human review when an agent encounters ambiguity. Work with UX and engineering on confidence signals and validation that let enterprise users understand the outcome.

• Own commercial and operational trade-offs

  Balance accuracy, latency and inference cost against viable margins and time-to-market, including when to use third-party APIs or proprietary orchestration. The posting also emphasizes privacy, auditability and uptime in regulated enterprise software.

Likely challenges to think through

• Drawing the agent–engine boundary

  Preparation hypothesis: an agent may help interpret an ambiguous task, while the existing engine must remain the source of deterministic calculations. Prepare to explain how you would work with tax SMEs to define tool boundaries and decide when the workflow should call the engine or request human judgment.

• Proving correctness without slowing delivery

  Preparation hypothesis: fast prototypes can demonstrate value before their failure modes are well understood. Prepare a way to turn raw-transcript failures into evaluation cases, distinguish prompt, tool, evaluation and model limitations, and use results to guide a launch decision.

• Keeping complex workflows commercially viable

  Preparation hypothesis: additional agent steps may improve task completion but increase latency and inference cost. Prepare to compare a multi-step path with simpler routing or deterministic alternatives, including when an explicit fallback is preferable to further model calls.

• Building trust without implying certainty

  Preparation hypothesis: visible human review or uncertainty signals can interrupt a workflow, but hiding them risks confidently wrong output in a regulated setting. Prepare to describe the user-facing trade-off and how privacy, auditability and uptime would shape the design.

Topics to prepare

• Agents versus deterministic tax engines

  The role explicitly calls for orchestrating agents around existing tax calculation engines, not recreating their logic in prompts. Revision exercise: sketch a tax-workflow boundary diagram showing which steps use an agent, which call the engine, and which require human review.

• Evaluation as a product specification

  The posting treats prototypes and evaluation suites as the definition of done, with accuracy, hallucinations and logic failures measured before launch. Revision exercise: outline pass/fail criteria and edge cases for a hypothetical agent-assisted tax task, leaving domain correctness to tax SMEs.

• Agent architecture and tool boundaries

  The role names multi-agent orchestration, state management, human-in-the-loop decisions, context windows, RAG, model routing and tool design. Revision exercise: compare a single-agent and multi-agent design for one ambiguous task; mark state transitions, permitted tool calls and escalation points.

• Accuracy, latency and unit economics

  The product manager must balance model accuracy, latency and inference cost against commercially viable margins. Revision exercise: compare a long agent loop with a shorter engine-assisted path, listing the quality, response-time and cost evidence needed to choose between them.

• Trust in regulated enterprise software

  The posting emphasizes privacy, auditability, uptime and UX that communicates uncertainty through visible fallbacks, confidence signals and human validation. Revision exercise: map what a user should see, what should be recorded and when review is needed if an agent cannot resolve an ambiguous request.

Skills to revise

• Prototype a bounded agent workflow

  The role calls for high-fidelity prototypes using agentic tooling such as Claude Code, Cursor or Copilot; production Python is not required. Practice: prototype a small illustrative workflow with an explicit calculation-engine handoff and a human-review branch, then explain what engineering would need to harden.

• Draft an evaluation suite

  The posting assigns the PM ownership of an eval harness built with engineering. Practice: write a compact set of representative, ambiguous and failure cases; specify expected outcomes and checks for unsupported claims, wrong tool use and logic errors.

• Diagnose a raw agent transcript

  The profile explicitly expects transcript debugging. Practice: inspect a sample run turn by turn, classify a failure as prompt, missing-tool, eval-gap or model-limitation related, and propose the first change to test.

• Translate SME judgment into constraints

  The role partners with tax SMEs to turn their judgment into system prompts, tool contracts and evaluation criteria; tax expertise is not expected. Practice: draft questions for an SME about an ambiguous task, then convert hypothetical answers into a tool-use rule and an eval case.

• Design a visible fallback interaction

  The posting requires predictable steps to engine rules or human review rather than confidently wrong answers or endless waiting. Practice: storyboard an uncertainty state, an engine fallback and a human-validation handoff, including the status shown to the user.

• Select a product opportunity

  Prepare a one-page example of how you would identify and validate an ambiguous tax workflow, including the user need, prototype hypothesis and measures of adoption, task accuracy and business impact. Treat the workflow as a proposed exercise, not a known company project.

• Sketch an agent prototype

  Draw a workflow showing agent responsibilities, state, tool contracts and human decision points. Show where an agent calls an existing tax calculation engine instead of reproducing deterministic tax logic in a prompt.

• Define an evaluation suite

  Draft sample test cases and pass criteria for accuracy, hallucinations and logic failures before a result reaches a client return. Note how you would work with engineering and tax subject-matter experts to establish correct outcomes.

• Walk through a transcript failure

  Prepare a real example, if available, of work you orchestrated with an agentic tool. Bring a sanitized transcript excerpt and explain whether the failure came from a prompt, missing tool, evaluation gap or model limitation, and what you changed.

• Design a visible fallback

  Sketch a user flow for an ambiguous or uncertain result: explain what the user sees, when the system uses calculation-engine rules or requests human review, and how it avoids a confident wrong answer or indefinite wait.

• Compare paths to production

  Prepare a brief trade-off table comparing a richer agent workflow with a cheaper alternative on accuracy, latency, inference cost and time to market. Add an example from your work, if available, showing how you handled privacy, auditability or uptime in enterprise software.

• Bring focused questions and rehearse independently

  Ask how the pod chooses its first opportunity, how SMEs and engineering agree on evaluation criteria, and what evidence supports a production launch. The posting says interviews are conducted without AI tools or external prompts, so practice explaining your examples unaided.