A Great Place to Upskill
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A Great Place to Upskill
Get the latest updates from Product Space
What changes when your AI stops answering questions and starts taking actions
A self-paced course on agentic AI and MCP for product managers: designing tools, permissions, evaluation, cost, UX, and risk for AI agents that take real actions.
The one property that makes something an agent
The autonomy ladder, five rungs, five different products
Why most things called agents aren't
What this course covers and what it doesn't
This module defines agentic AI and introduces the framework used throughout the course.
The loop: think, act, observe, repeat
Tools, and why they're the interesting part
Memory across steps, and what gets lost
Stopping conditions, the decision nobody assigns
The underlying mechanics of agent behavior, explained for product decisions rather than implementation.
The problem MCP solves, in one page
Servers, clients, and what actually gets exposed
Tools, resources and prompts, the three things a server offers
What MCP gives you, and what it definitely doesn't
An explanation of the Model Context Protocol at the level of detail a product manager needs.
Why tool design is product design
Naming, descriptions, and why your agent picks the wrong tool
Granularity: one big tool or six small ones
What a tool should return when it fails
How to design the tool inventory that defines what an agent can do.
Read, write, and irreversible, the only three categories that matter
Where to put the approval, and what it costs you
Approval fatigue, and how it quietly disables your safeguards
Designing the approval moment itself
How to design permission models and approval moments that hold up under real usage.
Why scoring the final answer isn't enough
Trajectory evaluation in plain language
Building a test set of tasks, not questions
The four things to measure on every run
How to evaluate agents on their trajectory rather than only their final output.
Why cost per task has a long tail
Where the seconds go in a multi-step agent
Loops, retries, and the runaway problem
Budgets, caps and what to do when one is hit
How to model and manage the cost and latency of agentic systems.
The waiting problem, thirty seconds of nothing
Showing work without showing everything
Interruption, correction and undo
Building trust one delegation at a time
How to design the user experience around a working agent.
The blast radius question, asked properly
Prompt injection when the agent can act
Compounding errors across steps
The incident you should rehearse before it happens
How to assess and limit the potential damage an agent could cause.
Five situations where a workflow beats an agent
The honest comparison: agent, workflow, or just a button
Tasks agents structurally cannot do reliably
Knowing when to reduce autonomy rather than add capability
How to judge when an agent is the right tool, and when it isn't.
Part 1: Scope, autonomy level and the tool inventory
Part 2: Permissions, approvals and blast radius
Part 3: An eval plan, a cost model and a ship recommendation
A complete design document for one agent, covering the decisions that are yours rather than engineering's.
Understanding the task
Designing tools
Permissions
Evaluating
Cost and caps
Experience and risk
Checking yourself
A reference collection of every prompt used throughout the course, organized by what you're trying to do.
Akhil Yash Tiwari
Founder
No. The course is designed for product managers with no coding background. Every technical concept, including MCP, tool design and agent loops, is explained before it's used, and the final project is a design document rather than working code.
The course is estimated at 14 to 18 hours in total, spread across 10 chapters plus a final project. It's self-paced, so you can work through it in short sessions or a few longer ones.
In a normal AI feature, you decide what steps happen. In an agentic system, the model decides what steps happen based on what it finds along the way. That single difference is what makes agents harder to specify, test and budget for.
MCP (Model Context Protocol) is an open standard for how AI applications connect to external tools and data. This course explains what MCP is and what it changes for your product decisions, but it does not teach you how to build an MCP server, that's an engineering task.
Product managers with some PM experience who want to ship products that take actions rather than just produce text. Familiarity with how large language models work is helpful but not required.
No. This is a self-paced written course, structured as reading plus exercises and copy-paste prompts you can run yourself, rather than video instruction.
A complete, real design document for one agent in your own product, covering scope and autonomy level, a tool inventory, a permission model, an evaluation plan, a cost model, and a ship recommendation.
any products marketed as "agents" are really workflows with an LLM inside, and workflows are often the right, cheaper, more predictable choice. Chapter 9 is dedicated to helping you judge honestly when a button or a workflow beats an agent.
The course uses one running example throughout, a freight forwarding platform investigating shipment exceptions, to keep every decision concrete. The frameworks and decisions taught apply to agentic products in any domain.
Each chapter includes a "copy this prompt" template you can run with your own AI assistant to work through the exercise, using your own product's real systems, tasks and constraints as the input.
