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Prompt Packs & Templates Intermediate

The Agent Scaffold Pack: 24 Ready-to-Run Agent Blueprints

Twenty-four agent blueprints with tool schemas, system prompts and real stop conditions.

Rated 4.5 out of 5 from 2 reviews 39 students

PR Created by Priya Raman

  • Last updated August 2026
  • English
  • 2h 10m of material
  • 16 lessons

What you will learn

7 concrete outcomes

Every bullet below is something you will have built, shipped or be able to explain by the time you finish the last lesson.

  • Design tool schemas tight enough that the model cannot misuse them
  • Give every agent a step budget, a token budget and a real stop condition
  • Carry state between turns without stuffing the whole history into context
  • Handle malformed tool calls and repeated calls without an infinite loop
  • Decide which actions an agent proposes and which it is allowed to perform
  • Log an agent run so you can reconstruct why it did something odd
  • Adapt a blueprint to your own domain without losing its safety rails

Course curriculum

5 modules · 16 lessons · 2h 10m of material

3 lessons running 22m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

3 lessons running 26m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

4 lessons running 34m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

3 lessons running 26m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

3 lessons running 22m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

5 modules · 16 lessons

2h 10m total length

Requirements

Short list, and deliberately so. If you meet these you can start today.

  • Comfortable writing Python and reading JSON schemas
  • You have called a model API with tool use or function calling at least once
  • An API key for Claude or another tool-capable model

About this course

Most agent demos work once and then quietly burn tokens forever. The difference between a demo and something you can leave running is rarely the model, it is the scaffold: which tools exist, what their schemas allow, how state is carried, and above all what makes the loop stop.

This pack contains twenty-four blueprints for agents that do narrow, useful jobs. A repository triage agent that labels issues and refuses to close them. A documentation gardener that proposes edits as a diff. A data cleanup agent bounded to one table. A support drafter that always leaves the send button to a human. A release checker that reads a pipeline and reports. Each blueprint ships with its system prompt, JSON tool schemas, a state shape, a budget in steps and tokens, an explicit stop condition, and a list of the things it is forbidden to do.

Sixteen recorded lessons build three of the blueprints from empty file to running loop in Python, including the parts nobody films: what happens when a tool call comes back malformed, when the agent decides to call the same tool eleven times, and when it invents an argument that was never in the schema.

Everything is delivered by an emailed access link, as Markdown documentation, JSON schema files and runnable Python skeletons you can drop straight into your own repository.

Topics covered

Frequently asked questions

Still unsure about something? Write to misteryjj100@gmail.com and a human answers, usually the same working day.

No. The skeletons use plain Python and the provider SDK. Notes are included for wiring the same blueprints into LangChain if your team already uses it.

By design, most propose rather than act. Every blueprint states which side of that line it sits on and how to move it deliberately if you need to.

Markdown documentation, JSON tool schema files, runnable Python skeletons and the sixteen lessons, all behind the access link emailed after checkout.

Checkout is handled on our provider's secure payment page. The moment your payment clears we email your personal access link and access code to the address you used at checkout, and the same link appears in your account library. There is nothing to install and nothing to wait for.

Email misteryjj100@gmail.com within 14 days of your purchase, quote your order number, and we refund the full amount to your original payment method. No form to fill in and no questions about how much of the course you watched.

What students say

Reviews are written by people who bought this course. We publish the critical ones too.

4.5

Rated 4.5 out of 5

Course rating · 2 reviews

Rating distribution

  • 5 stars 50%
  • 4 stars 50%
  • 3 stars 0%
  • 2 stars 0%
  • 1 star 0%
  • FR

    Federico Rossi

    AI engineer

    Mar 2026
    Rated 5.0 out of 5

    Stop conditions are the missing half of every demo

    Every agent tutorial shows you the loop running and then cuts away before it has to stop. Twenty-four blueprints that each specify a budget and an explicit condition for stopping is precisely the corrective the subject needed.
  • JP

    Ji-woo Park

    Software engineer

    Nov 2025
    Rated 4.0 out of 5

    Framework-agnostic in theory

    The blueprints are described as portable and mostly are, but all three worked examples are Python and the tool schema idioms lean that way throughout. Fine for me personally. A TypeScript equivalent would have made it five.

Your instructor

PR

Priya Raman

Applied AI and machine-learning engineer

  • 236 students taught
  • 13 courses published
  • 4.3 instructor rating
  • Machine learning
  • RAG
  • Fine-tuning
  • LLM agents

Priya builds language-model features for a document-heavy SaaS product, which means she has taken retrieval and fine-tuning from a promising notebook to something on call at three in the morning. She teaches the mathematics only where it changes a decision you are about to make, and spends the rest of the time on data quality, evaluation and the cost of an agent that loops. Her courses run on a laptop and a modest API budget, so nobody has to rent a cluster to follow along. She publishes reproducible notebooks alongside every module.

See every course by Priya Raman
$12 USD

One-time payment · lifetime access

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