The Complete 5-Day
AI Agents Crash Course

Preface

Why This Course Exists

In 15+ years of managing over $350M+ in digital advertising spend across Google Ads, Meta, Microsoft, and Amazon — at NortonLifeLock, Gen Digital, Avast, and Farmers Insurance — I've watched the industry transform three times. Programmatic buying. ML bidding. And now, the agentic era.

The industry is moving from AI that suggests to AI that acts. Google CEO Sundar Pichai declared at I/O 2025 that agentic capabilities represent "the direction where we are investing the most," and predicted 2026 as "the year people use agentic experiences more broadly" (Alphabet Inc., 2025).

I built ahmeego.com — 28 API actions, 6 sub-agents, live Google Ads access, built on Claude — because this future was arriving faster than practitioners realized.

How to Use This Course

Sequential: Section 0 → Day 1 → Day 5. Best for first-time learners.

Reference: Sidebar navigation. Each section is self-contained.

Workshop: Each day has labs, games, and assessments — classroom-ready.

Quick scan: Executive Summary + Glossary + Resource Library.

Acknowledgments

Google's Kaggle team (Wiesinger, Marlow, Vuskovic) for the white paper series. Anthropic's MCP and Claude. Google's A2A team. LangChain, CrewAI, AutoGen communities. Microsoft's AI Agents course. Colleagues at Seer Interactive and Casteel High School coaching staff.

Executive Summary

AI agents — autonomous systems that reason, plan, and execute — are replacing dashboards and manual optimization. Google, Anthropic, OpenAI, and Microsoft are investing billions.

This 5-day course covers: (1) Agent fundamentals and ReAct loops; (2) Tool architecture and MCP; (3) Context engineering and scratchpad patterns; (4) Evaluation, observability, and guardrails; (5) Multi-agent orchestration, A2A, and Google's Universal Commerce Protocol.

Each day: required readings · resource matrix · hands-on labs · screen verification callouts · vibe coding game · learning objectives · key takeaways · self-assessment.

Three paths (🟢 Observer, 🔵 Builder, 🟣 Architect). Free. Open source. 42 pages. APA 7th Edition.

Section 0: Complete Environment Setup

Install and verify all tools before Day 1. Time: 30–45 minutes.

Part A: Claude AI

Primary AI assistant. Powers ahmeego.com.

Verification

You are a Research Agent. Explain what an AI agent is in\n3 sentences as if speaking to a colleague at a coffee shop,\nthen provide one real-world example.COPY

Part B: ChatGPT

Part C: Cursor IDE

"Vibe coding" — describe what you want, AI writes the code (Karpathy, 2025).

Part D: Kaggle

Part E: GitHub

Part F: Python 3.12+

Pre-Course Checklist

All verified? → Day 1.

Day 1: Introduction to AI Agents

What Is an AI Agent?

An AI agent is software that uses a large language model as its reasoning engine to autonomously perceive, decide, and act to accomplish goals (Google, 2025). The key difference from a chatbot is autonomy: agents decompose goals, select tools, execute, evaluate, and iterate.

Analogy: Ask a chatbot about vacation → it describes beaches. Give an agent the same request → it researches flights, compares prices, books the optimal option, reserves a hotel, adds calendar entries, sends confirmation. The chatbot generates text. The agent generates outcomes.

The Three Components

1. The Brain (Foundation Model)

Claude, GPT, or Gemini — the reasoning engine. Interprets intent, formulates plans, selects tools. In ahmeego.com: Claude Sonnet 4.6 as the baseline, with user-selectable models including GPT-5.4-pro and Gemini 3 Flash.

2. The Tools

Executable functions: APIs, databases, search engines, file systems. Without tools, the model reasons but cannot act. ahmeego.com: 28 Python API actions in actions/main-agent/.

3. The Loop (ReAct Orchestration)

Reason → Act → Observe → Repeat. ahmeego.com's "The Loop":

Chatbot vs. Agent

Architecture Patterns

Required Reading

Primary: Google & Kaggle. (2025). "Introduction to Agents." kaggle.com/whitepaper-agents ↗

Supplementary: Microsoft. AI Agents for Beginners, Lessons 1–3. GitHub ↗

Resource Matrix

Lab 1A: Agent-Style Reasoning

You are a Google Ads Audit Agent. A client runs 15 Search\ncampaigns at $50,000/month. CPA has increased 40% over 3 months.\n\nCreate a systematic audit plan. For each step, specify:\n(a) The exact data required\n(b) The API endpoint or tool to retrieve it\n(c) Metrics and red flags to evaluate\n(d) Conditional recommendations based on findingsCOPY

🎮 Vibe Coding: Tetris

Build a fully playable Tetris game as a single HTML file.\n- Classic rules: 7 tetrominoes, rotation, line clearing\n- Arrow keys to move/rotate, spacebar for hard drop\n- Score, next piece preview, level progression\n- Game over + restart, dark bg, smooth animations\n- Ghost piece showing landing positionCOPY

AI Agents Across Ad Platforms

The Brain + Tools + Loop pattern applies to every major advertising platform. Each platform has its own API, data structures, and optimization levers — but the agent architecture is identical.

Google Ads: Where Agents Shine

Google Ads is the most agent-ready platform because of its mature API, rich data model (GAQL), and complex optimization surface. ahmeego.com demonstrates this with 28 live API actions covering:

• Search Term Analysis — Mining thousands of queries for negatives and expansion opportunities
• RSA Ad Generation — AI writes headlines and descriptions matching brand voice and editorial policies
• Budget Reallocation — Shifting spend from underperforming to high-ROAS campaigns automatically
• Quality Score Optimization — Analyzing landing page relevance, ad relevance, and expected CTR
• Performance Max — Asset group strategy, audience signal configuration, insights interpretation

Meta Ads: Creative Intelligence

On Meta, the #1 performance lever is creative — and AI agents excel at creative analysis and generation:

• Creative Fatigue Detection — Monitoring frequency and CTR decay to recommend asset refreshes
• Dynamic Creative Optimization — AI selects optimal headline + image + CTA combinations
• Advantage+ Shopping — Letting Meta's AI handle targeting while agents monitor performance bounds
• Conversions API (CAPI) — Server-side event tracking for better attribution in the cookieless era

Day 2: Agent Tools & MCP

The Tool-Use Cycle

Tool Categories in ahmeego.com

Model Context Protocol (MCP)

MCP is an open standard by Anthropic (Nov 2024) providing a universal interface for connecting AI to tools. Analogy: USB-C — before it, every device needed a unique cable. Before MCP, every AI app needed custom integration code. MCP standardizes the connection.

MCP Architecture

Host: The AI application (Claude Desktop, Cursor). User-facing interface.

Client: Embedded in the host. Manages connections to MCP servers.

Server: Lightweight service exposing: Tools (functions), Resources (data), Prompts (templates).

Resource Matrix

Lab 2A: Observing Tool Use

Search the web for the latest MCP announcements from the past\n30 days. Summarize the top 3 developments and analyze\nimplications for digital advertising.COPY

Lab 2B: Design a Tool Schema

Design a tool schema for a Google Ads Search Term Analyzer.\nSpecify: (1) tool name + description, (2) typed input parameters,\n(3) structured output format, (4) error cases.\nFormat as JSON for an MCP server definition.COPY

🎮 Vibe Coding: Zelda Adventure

Build a Zelda-style top-down adventure game as a single HTML file.\n- Arrow keys/WASD movement, spacebar sword attack\n- Overworld: grass, trees, water, paths\n- 3+ enemy types with patterns and damage\n- 3 hearts, rupees, health pickups\n- Multiple connected rooms with edge transitions\n- Mini-map showing explored rooms\n- SNES pixel-art aestheticCOPY

Tools Across Advertising Platforms

Each advertising platform exposes tools through its API. Here's how agents interact with the major platforms:

Google Ads API — GAQL in Practice

Google Ads Query Language (GAQL) is how agents retrieve data. Instead of clicking through the UI, an agent writes structured queries:

SELECT campaign.name, metrics.cost_micros, metrics.conversions,
       metrics.cost_per_conversion
FROM campaign
WHERE segments.date DURING LAST_30_DAYS
  AND metrics.cost_micros > 0
ORDER BY metrics.cost_per_conversion DESC
LIMIT 10COPY

ahmeego.com's Search Term Manager uses GAQL to pull thousands of search queries, then Claude analyzes intent patterns and recommends negatives. This is the same pattern any MCP-connected agent could use.

Meta Marketing API — Creative Analysis

Meta's API enables agents to monitor creative performance and detect fatigue:

# Meta Marketing API — Ad creative insights
GET /act_{ad_account_id}/insights
  ?fields=ad_name,impressions,clicks,spend,
          actions,cost_per_action_type,
          frequency,video_avg_time_watched_actions
  &date_preset=last_30d
  &level=ad
  &filtering=[{"field":"impressions","operator":"GREATER_THAN","value":1000}]COPY

An agent monitors frequency (>3.0 signals fatigue), CTR decay (>20% drop week-over-week), and cost per result trends to automatically flag creatives that need refreshing.

Google Ads Scripts — Accessible Automation

For practitioners who aren't developers, Google Ads Scripts offer the most accessible automation entry point. Scripts run directly in the Google Ads UI:

// Pause keywords with high CPA and no conversions (30 days)
function main() {
  var keywords = AdsApp.keywords()
    .withCondition("Conversions = 0")
    .withCondition("Cost > 50")
    .forDateRange("LAST_30_DAYS")
    .get();
  while (keywords.hasNext()) {
    var kw = keywords.next();
    kw.pause();
    Logger.log("Paused: " + kw.getText() + " ($" + kw.getStatsFor("LAST_30_DAYS").getCost() + ")");
  }
}COPY

AI-Powered Tool Frameworks

Day 3: Context Engineering

What Is Context Engineering?

The discipline of designing the complete information payload that flows into an agent's prompt at each step. It determines what the agent knows, remembers, and how it personalizes behavior.

Six Layers of Context

The Scratchpad Pattern

Instead of raw conversation history (noisy, overflows context), the agent maintains a structured working document. In ahmeego.com: 🦁 Simba writes to Reporting section, ❄️ Elsa to Optimization, 🐠 Nemo to Research. Final synthesis reads the complete scratchpad.

RAG: Retrieval-Augmented Generation

Query a vector database, retrieve only semantically relevant chunks. Grounds responses in source material rather than potentially outdated training data. Scales to massive knowledge bases.

Memory Strategies

🎮 Vibe Coding: Football (Tecmo Bowl)

Build an American football arcade game (Tecmo Bowl style)\nas a single HTML file.\n- Top-down field with yard lines and end zones\n- Arrow keys control QB/ball carrier\n- 5 offense vs 5 defense players\n- Play selection screen (4 plays)\n- Spacebar to pass, arrows to aim\n- Tackle detection, scoring, downs system\n- Touchdown celebration, 8-bit aestheticCOPY

Context Engineering for Advertising

Advertising agents need specialized context that general-purpose agents don't. Here's what makes ad context engineering unique:

Prompt Engineering for Ads

Before context engineering, you need solid prompts. The four essential patterns for advertising agents:

RAG for Advertising Knowledge

RAG (Retrieval-Augmented Generation) is critical for grounding ad agents in current, accurate data:

• Google Ads documentation — Policy changes, new features, editorial requirements (changes quarterly)
• Industry benchmarks — CPC/CPA/CTR by vertical, refreshed monthly
• Account history — Previous audits, seasonal patterns, historical performance baselines
• Competitive intelligence — Ad Library data, auction insights, market share trends
• Platform changelogs — Google, Meta, Microsoft release notes that affect strategy

Context Across Platforms

Day 4: Agent Quality

The Quality Problem

A model hallucinating 5% of the time in casual conversation = minor annoyance. Hallucinating 5% while managing $50,000/month in ad spend = financial liability. Agent quality engineering closes this gap.

Three Pillars

Pillar 1: Evaluation

Unit: Test individual tool calls. Does Search Term Manager return accurate data?
Trajectory: Test reasoning path. Did it select the right tools in the right order?
Final Response: Is the audit report accurate and actionable?

Pillar 2: Observability

Logging: Timestamped records of every tool call and decision (flight recorder).
Tracing: End-to-end path from input through all tool calls to output.
Metrics: Latency, token usage, tool success rate, accuracy.

Pillar 3: Guardrails

Input: Validate requests fall within authorized scope.
Output: Verify responses for accuracy before delivery.
Action: Require human approval for high-impact operations. ahmeego.com's CONFIRM protocol: displays current vs. proposed, explains rationale, waits for confirmation before executing.

LLM-as-a-Judge

A separate LLM scores agent outputs against rubrics (accuracy, completeness, relevance, safety). Returns structured scores with justification. High correlation with human evaluators at dramatically lower cost.

Resource Matrix

🎮 Vibe Coding: Super Mario Bros

Build a Super Mario Bros-style platformer as a single HTML file.\n- Side-scrolling, arrow keys + spacebar jump\n- Platforms, pipes, bricks, question-mark blocks\n- Breakable blocks yield coins from below\n- 2+ enemy types, jump to defeat\n- Coin counter, score, pit death, 3 lives\n- Flag pole to complete level\n- NES-era physics: momentum, gravity, accelerationCOPY

Quality for Advertising AI

Advertising introduces unique quality challenges that general AI applications don't face:

AI Safety in Ad Management

Creative Quality with AI

AI-generated ad creative needs specialized quality checks:

• Editorial compliance — Google, Meta, and Microsoft each have different ad policies
• Brand consistency — AI-generated copy must match brand voice, tone, and terminology
• Character limits — Google RSA: 30-char headlines, 90-char descriptions; Meta: 40-char primary text recommended
• Image compliance — Text overlay limits (Meta <20%), brand safety, accessibility (contrast ratios)
• A/B test validity — Sufficient sample size before declaring winners; statistical significance thresholds

Search Query Quality System

One of the highest-value quality applications is automated search query analysis — the system ahmeego.com calls SQOS (Search Query Optimization System):

Conversion Tracking Quality

Agents can also audit measurement infrastructure — the Marketing Analytics Auditor on ahmeego.com demonstrates this:

• GA4 event validation — Verify all conversion events fire correctly
• GTM container audit — Check for duplicate tags, missing triggers, consent mode compliance
• Cross-platform attribution — Compare Google, Meta, and GA4 conversion counts for discrepancies
• Server-side tracking — Validate CAPI (Meta) and enhanced conversions (Google) implementation

Day 5: Production & Multi-Agent Systems

The Prototype-to-Production Gap

Building a demo = cooking for friends. Deploying production = opening a restaurant.

Agent-to-Agent Protocol (A2A)

Google's open standard (April 2025) enabling agents to communicate regardless of framework or vendor. If MCP = USB-C for tools, A2A = telephone network for agents.

MCP vs. A2A

Architecture Walkthrough: "Audit my Google Ads account"

Google's Agentic Commerce Vision

🎮 Vibe Coding: Duck Hunt

Build a Duck Hunt-style shooting gallery as a single HTML file.\n- Click to shoot flying ducks\n- Randomized flight patterns, crosshair cursor\n- Gunshot sound + muzzle flash (Web Audio API)\n- Hit ducks tumble, dog retrieves\n- 10 ducks/round, 3 rounds, increasing difficulty\n- 3 shots per duck, score + accuracy %\n- Classic NES aestheticCOPY

🎮 Capstone: AI Game Master RPG

Build an AI-powered text RPG Game Master as a single HTML file.\n- Character creation: name, class (Warrior/Mage/Rogue)\n- Stats: HP, Attack, Defense, Magic, Level, XP\n- Inventory with equippable items + consumables\n- Turn-based combat with enemy AI\n- Adaptive narrative (3+ branching points)\n- Shop between encounters\n- Boss encounter, HP bars, dark-fantasy styling, save/loadCOPY

Multi-Platform Agent Architecture

Production advertising agents don't manage just one platform. The real power emerges when agents orchestrate across Google, Meta, Microsoft, and Amazon simultaneously.

Cross-Platform Budget Orchestration

The most valuable production use case is AI-driven budget allocation across platforms:

Automated Reporting Across Platforms

The highest-value, most immediately adoptable AI use case for agencies is automated cross-platform reporting:

• Natural language reports — Agent pulls data from Google, Meta, Microsoft APIs → Claude generates executive summary
• Anomaly detection — Flag CPA spikes, budget pacing issues, and conversion drops across all platforms
• Slack/email alerts — Real-time notifications when metrics breach thresholds
• Client-facing dashboards — AI-generated insights alongside raw data in Looker Studio or custom UIs

Building AI Ad Tools as Products

The journey from script → tool → product is how ahmeego.com was built. Key production considerations:

AI Tools You Can Use Today

Appendix A: Complete Resource Library

Google/Kaggle White Papers

All 5 compiled: github.com/sameeerjadhav/google-agents-resources ↗

Free Courses

Protocols

Frameworks

Observability

Appendix B: Glossary

22 key terms.

Appendix C: ahmeego.com Technical Reference

Sub-Agent Specifications

All 28 Actions

References (APA 7th Edition)

About the Author

John Williams is a Senior Paid Media Specialist at Seer Interactive with 15+ years managing $350M+ in digital advertising spend across Google Ads, Meta, Microsoft, Amazon, and other platforms. Previous: NortonLifeLock (192% YoY paid search growth), Gen Digital, Avast, Farmers Insurance.

Creator of ahmeego.com — open-source AI agent with 28 API actions and 6 sub-agents. Operates "It All Started With A Idea," his consultancy and product studio. Speaker at Hero Conf 2025 + 2026. 19+ open-source marketing automation tools on GitHub.

Assistant football coach at Casteel High School (WR, DB, special teams). Former Washington State University football player (2002–2005).

This course is free and open source. Share it. Teach it. Fork it.
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Phase 2 covers deep-dive platform tracks (Google Ads API, Meta Marketing API, Microsoft Ads, Amazon Ads). Phase 3 covers cross-platform budget orchestration, MCP server development, LangChain/CrewAI, and deploying AI tools as products. 2 modules are already LIVE ON SITE as tools: Creative Asset Validation and Conversion Tracking.