Professional AI Agent Product Management

Map an existing user workflow into goals, decisions, actions, tools, and failure points.

Convert user-research evidence into an agent-specific opportunity statement.

Score a candidate agent use case for value, feasibility, verifiability, and consequence of error.

Define which workflow decisions belong to the agent and which remain with a human.

Write a testable value hypothesis for an agent product.

Define measurable discovery criteria for deciding whether to prototype an agent use case.

Diagram how a model, instructions, context, tools, and an orchestration loop form an agent system.

Explain how context-window limits affect an agent’s behavior and product design.

Specify a structured input and output contract for an agent tool.

Explain how tool calling lets an agent take actions in external systems.

Select retrieval, session state, or persistent memory for a given product need.

Read basic API documentation and identify the information needed to connect an agent tool.

Trace data and control flow through a single-agent architecture.

Define a narrow end-to-end task for the course’s example agent prototype.

Write system instructions that establish the prototype’s role, constraints, and completion criteria.

Create a structured output schema for one prototype action.

Connect the prototype to a mock or low-risk external tool using a vibe-coding platform.

Implement session state for a multi-step prototype interaction.

Inspect an execution trace to locate a prompt, reasoning, or tool-use failure.

Revise one prototype component and verify that the targeted failure is corrected.

Create a taxonomy of model, tool, data, and user-interaction failures for an agent workflow.

Build a compact evaluation set containing normal, edge, and adversarial cases.

Write a scoring rubric for task completion, correctness, and policy compliance.

Run a repeatable offline evaluation of an agent prototype.

Compare an agent result against a non-agent baseline using the same success criteria.

Set release thresholds and guardrail metrics based on product risk.

Diagnose an evaluation failure and assign it to the appropriate product or technical intervention.

Map sensitive data across an agent’s inputs, model calls, memory, tools, and outputs.

Identify prompt-injection and tool-misuse risks in a sample agent workflow.

Specify least-privilege permissions for an agent’s tools and data access.

Place human approval checkpoints before consequential or irreversible actions.

Design a safe fallback and escalation path for low-confidence or failed actions.

Document a product-level safety case that links key risks to mitigations and evidence.

Scope a minimum viable agent around one valuable and verifiable workflow.

Design user-visible states for planning, acting, waiting, failing, and completing.

Choose where the experience should expose sources, uncertainty, progress, and agent actions.

Write an agent behavior specification covering goals, boundaries, tools, and escalation rules.

Write acceptance criteria that account for variable model outputs.

Prioritize an agent backlog using user value, evidence, technical feasibility, and risk.

Create a concise PRD that aligns design, engineering, data, legal, and go-to-market stakeholders.

Compare candidate models using task quality, latency, cost, privacy, and operational constraints.

Make a build, buy, or partner recommendation for one agent-system component.

Break an agent MVP into coordinated product, design, engineering, evaluation, and governance workstreams.

Define ownership and decision rights for cross-functional agent-product work.

Create a versioning plan for prompts, models, tools, and evaluation sets.

Conduct a structured pre-launch red-team exercise.

Facilitate a go/no-go decision using evaluation evidence and unresolved-risk criteria.

Select a limited beta cohort that can test value while containing product risk.

Position an agent product around a measurable user outcome rather than model capabilities.

Design onboarding that establishes capabilities, boundaries, and appropriate user trust.

Instrument the agent journey from task initiation through verified completion.

Define a north-star metric with quality, safety, cost, and latency guardrails.

Create a staged rollout plan with rollback triggers.

Design a feedback loop that converts user reports and behavioral data into prioritized improvements.

Interpret a post-launch dashboard to distinguish adoption, quality, reliability, and economics problems.

Triage a production agent incident using impact, urgency, and containment criteria.

Choose an intervention that balances task quality, latency, and inference cost.

Plan a post-launch experiment from observed failure through a measurable decision.

Assemble a portfolio case study covering discovery, prototype, evaluation, safety, delivery, and launch decisions.

Present an evidence-based product narrative and defend key agent-product trade-offs to stakeholders.