A Great Place to Upskill
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© ACADENCE EDUTECH PRIVATE LIMITED
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
