Professional AI Agent Product Management

Map a provided or learner-selected user workflow into goals, decisions, actions, tools, and failure points.

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

Score candidate agent use cases for value, feasibility, verifiability, and consequence of error, then select one course use case.

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

Write a testable value hypothesis linking the agent use case to user and product outcomes.

Define measurable evidence thresholds for deciding whether the selected use case should advance to prototyping.

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.

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

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

Read basic API documentation and identify relevant authentication, endpoint, schema, and usage-limit information.

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

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

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

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

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

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

Write acceptance criteria that account for variable model outputs.

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

Draft the decision-critical sections of a concise PRD that aligns design, engineering, data, legal, and go-to-market stakeholders.

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.

Draft a product-level safety assurance plan linking key risks to mitigations and the evidence that must be collected.

Translate the selected product’s behavior specification and safety controls into a minimal implementation plan.

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 with restricted permissions using a vibe-coding platform.

Implement session state for a multi-step prototype interaction.

Inspect an execution trace to locate an instruction, context, state, 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.

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, red-team, and safety evidence plus explicit unresolved-risk criteria.

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

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

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

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

Instrument the agent journey from task initiation through verified completion.

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.