AI Agents Full Course 2026: Master Agentic AI (2 Hours)
AI agents aren't just large language models in a wrapper—they're orchestrated systems capable of parallel execution, self-correction, and economically valuable work at scale. This course promises to teach you the foundational loop that powers all agent platforms, advanced prompting architectures that spawn multiple agents to debate and verify each other's work, and techniques for managing context windows as token counts climb into the hundreds of thousands. The question isn't whether agents can replace repetitive tasks; it's whether you'll learn to architect them before your competitors do.
Punti chiave
The core agent loop—observe, reason, act—repeats until a clearly defined «definition of done» is met; vague tasks produce poor results, while prompt contracts and reverse prompting force clarity before execution.
Parallelization is the superpower: spawn multiple agents with slight prompt variations to traverse the search space faster, catch rare outlier ideas, and reduce wall-clock time from hours to minutes.
Self-modifying system prompts (agents.md, claude.md, gemini.md) accumulate rules over sessions, reducing errors asymptotically as the model learns your preferences without token pollution.
Sub-agent verification loops eliminate sunk-cost bias: one agent builds, a fresh agent with zero context reviews, and a resolver fixes issues—higher quality output with minimal human oversight.
The 60/30/10 rule (Haiku for simple tasks, Sonnet for mid-tier, Opus for routing) cuts token costs by 60% with only marginal quality trade-offs, critical at enterprise scale.
In breve
Mastering AI agents means understanding they are statistical machines best deployed in parallel, with carefully managed context, self-modifying prompts, and multi-agent orchestration—trade a few percentage points of quality for massive cost savings and speed, and you unlock economically transformative automation.
The Five-Browser Demo: Parallel AI Agents in Action
Multiple Claude instances autonomously fill contact forms across different websites simultaneously.
The course opens with a live demonstration: five Chrome browsers, each controlled by an independent AI agent, navigating to company websites, locating contact forms, and dynamically filling fields with personalized outreach—all in parallel. Each agent operates in its own workspace, communicates via a shared chat room, and adjusts messaging based on research scraped from the target site. What one agent might take hours to do sequentially, five accomplish in minutes. This isn't speculative—it's the end state you'll build toward by mastering multi-agent orchestration, parallelization, and shared context protocols.
The Core Agent Loop: Observe, Reason, Act
All agents iterate through observation, reasoning, and action until the definition of done.
Observe The agent reads all available context: files, previous tool calls, system prompts, research results, and multimodal inputs like vision or audio.
Reason The agent plans its next move in a dedicated «thinking» step, visible in most platforms, allowing you to steer or abort mid-execution.
Act The agent calls tools, edits files, runs commands, or searches the web, then feeds the result back into the observe step to grow the context window.
Loop until done The cycle repeats—each iteration stacking more tokens—until the agent reaches the «definition of done» specified in your prompt.
Output The agent generates a final, formatted response and packages the deliverable in a user-friendly window (Codex, Claude Code, or Anti-Gravity).
Platform Setup: Codex, Claude Code, Anti-Gravity
Model Intelligence: Claude vs. Gemini vs. GPT
Marginal quality differences exist, but all three are intelligent enough for most tasks.
Self-Modifying System Prompts: agents.md, claude.md, gemini.md
Agents rewrite their own rules to minimize errors across sessions.
Every conversation begins with a hidden file—agents.md for Codex, claude.md for Claude Code, gemini.md for Anti-Gravity—prepended to the top of the context. These files store evolving rule sets. When you correct the agent or it makes a mistake, it appends a new rule: «Never use dark mode,» «Always use Vite for front-end builds,» «Avoid Bootstrap templates.» Over time, the number of preference violations drops asymptotically. Session one might produce five errors; session five produces zero. This is sunk learning: the agent accumulates institutional memory without polluting the active context window. You can nest global and local files (user-wide preferences plus project-specific rules) and layer in «skills» for repeatable workflows.
Agent Skills: Standardized, Repeatable Workflows
Skills turn flexible LLMs into deterministic pipelines for consistent output.
Agent Skills: Standardized, Repeatable Workflows
Large language models are inherently stochastic—ask the same question twice, get two different answers. Skills solve this by hardcoding workflows into Markdown files (name, description, steps, tools) stored in your workspace. Want algorithmic art? Copy a skill spec, and the agent generates the same P5.js template every time. Want PDF processing? One skill, one result. Skills collapse token usage, reduce variance, and let you share SOPs across teams without re-explaining context.
Multi-Agent MCP Orchestration: Delegate to Specialists
One orchestrator routes tasks to multiple models based on their strengths.
Why settle for one model when you can exploit the marginal advantages of three? Multi-agent MCP orchestration uses Claude as a manager: it receives your high-level task, breaks it into subtasks, and delegates. Front-end UI? Routed to Gemini. Backend API and testing? Routed to Codex. Video analysis? Gemini's multimodal endpoints. The orchestrator then collects results, validates integration, and fixes discrepancies. You pay a token premium (API calls aren't subsidized like monthly plans), but you gain speed through parallelization and quality through specialization. This pattern shines at the bleeding edge, where 2–5% quality improvements compound into meaningful differentiation.
Video-to-Action Pipelines: Teaching Agents from YouTube
Stochastic Multi-Agent Consensus: Traverse the Search Space
Spawn ten agents with slight prompt variations to surface rare, high-value ideas.
One agent returns ideas A, B, C. Run it again: A, B, D. Again: B, C, E. Statistical variance is usually a bug; here it's a feature. Stochastic multi-agent consensus spawns five to ten agents in parallel, each with slightly different framing (conservative, optimistic, user-focused, contrarian). They independently analyze the same problem, then report back. The orchestrator calculates mode (consensus ideas), median (average quality), and outliers (rare wild cards). You traverse 3–5× more of the possibility space in the same wall-clock time. This technique excels for ideation, strategic analysis, and filtering hallucinations—quantity becomes quality when you can parallelize cheaply.
Agent Chat Rooms: Debate for Higher Quality
Agents with opposing personas debate solutions, sharpening ideas through conflict.
Assign personas Spawn five agents: Systems Thinker, Pragmatist, Edge Case Finder, User Advocate, Contrarian—each with a distinct analytical lens.
Round-robin debate Agents take turns in a shared chat.json file, challenging each other's assumptions and proposing alternatives over multiple rounds.
Consensus + divergence The orchestrator identifies agreed-upon conclusions, unresolved disagreements, and one-off insights that might be brilliant or hallucinated.
Synthesis A final summary ranks ideas by confidence, flags risks, and delivers a nuanced report that one agent alone could never produce.
Sub-Agent Verification Loops: Fresh Eyes, Zero Bias
One agent builds, another reviews with zero context, a third resolves issues.
Prompt Contracts: Define Success Before You Start
Reverse Prompting: Ask Before Assuming
The agent asks five clarifying questions before building anything.
Reverse Prompting: Ask Before Assuming
Reverse prompting inverts the workflow: instead of you specifying every detail, the agent analyzes your request, identifies implicit assumptions and decision points, then asks five dynamically generated questions. «Primary goal: brand credibility or lead-gen?» «Vibe: Linear-clean or something edgier?» «Generate copy or use placeholders?» Once you answer, it constructs a prompt contract with those preferences baked in. One-shot accuracy skyrockets because the agent surfaced non-obvious constraints you didn't even know you had.
Multi-Agent Chrome MCP Manager: Parallelized Browser Automation
Spawn ten Chrome instances, each controlled by a sub-agent, to fill forms 10× faster.
The opening demo, deconstructed: an orchestrator (Claude Opus) receives a high-level task—fill contact forms for 1,000 leads. It determines how many agents are needed (e.g., ten), spawns independent Chrome DevTools MCP servers for each, and distributes target URLs. Each sub-agent operates in its own workspace with its own browser: navigate, screenshot, identify form, fill fields, submit. All run in parallel. Wall-clock time: 2 minutes per agent, but ten agents = ten forms in 2 minutes = 5 forms/minute. Scale to 100 agents, and you hit 2,000 submissions in 40 minutes instead of 66 hours. The orchestrator monitors a shared chat.json file, checks for errors every 30 seconds, and reallocates failed tasks. This is the ultimate expression of agent parallelization.
Context Window Management: The Iceberg Technique
Key Numbers: Token Costs and Agent Pricing
Strategic model selection cuts costs 60% with minimal quality loss.
The 60/30/10 Token Allocation Strategy
Use dumb models for simple tasks, smart models only when necessary.
60% → Haiku (simple) Bulk scraping, email classification, contact form detection—tasks that were solved two generations ago. ~$1/1M tokens.
30% → Sonnet (mid-tier) Lead enrichment, outreach generation, code review—tasks requiring moderate reasoning. ~$3/1M tokens.
10% → Opus (routing + complex) Orchestration, multi-agent routing, high-stakes decisions, final quality review. ~$15/1M tokens.
Result Same 100M token workload drops from $500 (all Opus) to $200 (mixed allocation)—60% savings, ~5–10% quality trade-off on aggregate.
Final Principles: Architecture > Intelligence
The wrapper around the LLM is more important than the model itself.
Final Principles: Architecture > Intelligence
A year ago, you might have thought an AI agent was just a large language model in a chat interface. Now you know better. The model is the reasoning engine, but the agent is the system: tools (MCP, Chrome DevTools, file I/O), memory (self-modifying prompts, skills, context), orchestration (parallelization, routing, verification loops), and context management (iceberg technique, compression, on-demand loading). The architecture you build around the intelligence determines whether your agent produces value or burns tokens. Master the loop, the contracts, the multi-agent patterns—and you unlock automation that scales faster than hiring.
Persone
Glossario
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