Recap of the 5 Modules
Connecting the concepts
Before moving on to the Masterclass, let’s consolidate the journey so far and understand how each module connects to the next.
Context Engineering
A prompt is not text — it is context system. We learned to structure context in layers (System, Global, Task, Data) with a clear hierarchy.
Long Context Management
With 100K-1M+ token windows, we've learned to organize large volumes of context without degrading the quality of responses.
Skill Engineering
Prompts such as modular, reusable skills. Specialized skills that can be activated, composed, and versioned.
Orchestration
Combining contexts and skills in complex workflows. Conflict resolution, skill chains, and context-driven systems.
Modern RAG
RAG as complement, not a dependency. Hybrid context, selective injection, and grounding-based anti-hallucination strategies.
The Synthesis
Together, these modules form a new way of thinking about AI: Context Engineering + Skills = Intelligent Agents. The Masterclass will apply this to autonomous production systems.
Agent Engineer Mindset
Systems thinking for AI
The Shift in Perspective
An Agent Engineer is not an "advanced prompt engineer." It is a discipline of systems design where the central component is an LLM.
❌ Old-School Mindset
- • "How do I make the model respond with X?"
- • "What magic prompt solves this?"
- • "The model got it wrong; I need a better prompt"
- • Focus on specific words
✓ Agent Mindset
- • "What context does the agent need?"
- • "Which skills should be available?"
- • "Is the context system complete?"
- • Focus on architecture and flows
Fundamental Principles
Before writing any instructions, define the necessary context. Context determines 80% of the response quality.
Build reusable skills, not one-off instructions. Skills can be tested, versioned, and composed.
Don't adjust prompts indefinitely. Iterate on the system design: flows, fallbacks, orchestration.
Every claim must have a source. Every system must have metrics. Without measurement, there is no improvement.
Consolidated Patterns
Patterns that work in production
Emerging patterns in production agent systems that you will apply in the Masterclass.
Layered Context
Context organized in layers with defined priorities and scopes.
Skill Registry
Catalog of available skills with triggers and context requirements.
Context Router
Intelligent routing based on intent and available context.
Grounded Response
Answers always grounded in verifiable sources from the context.
Context Budget
Token allocation and management by context layer.
Skill Chain
Composition of skills with context passing between steps.
Standard Agent Architecture
┌────────────────────────────────────────────────────────┐ │ AGENT CORE │ ├────────────────────────────────────────────────────────┤ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Context │ │ Skill │ │ Memory │ │ │ │ Manager │ │ Registry │ │ Store │ │ │ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │ │ │ │ │ │ │ └────────┬───────┴────────┬───────┘ │ │ │ │ │ │ ┌──────▼──────┐ ┌──────▼──────┐ │ │ │ Router │ │ Orchestrator│ │ │ └──────┬──────┘ └──────┬──────┘ │ │ │ │ │ │ └───────┬────────┘ │ │ │ │ │ ┌──────▼──────┐ │ │ │ LLM │ │ │ │ (Core) │ │ │ └─────────────┘ │ └────────────────────────────────────────────────────────┘
Anti-Patterns to Avoid
Common errors in agent systems
God Prompt
A giant prompt that tries to do everything. Impossible to maintain, test, or debug.
Context Dump
Throw all available information into the context without organization or prioritization.
RAG-First
Use RAG by default when it would be more efficient and accurate than Long Context.
Skill Spaghetti
Skills with cross-dependencies, no clear interface, or defined contract.
Hallucination Denial
Try to fix hallucinations with "don't hallucinate" or longer instructions.
Integrated Case Study
Applying all the concepts
Scenario: Code Review Agent
Let’s design a code review agent that applies all the patterns learned.
1. Context System
identity: "Code Review Agent" model: "claude-3-opus" capabilities: [analyze_code, suggest_fixes, check_style] project_conventions: "{coding_standards}" tech_stack: [Python, FastAPI, PostgreSQL] style_guide: "{style_guide_content}" pr_diff: "{diff_content}" related_files: ["{file_contents}"] recent_reviews: ["{last_3_reviews}"]
2. Available Skills
Detects common vulnerabilities
Checks compliance with the style guide
Identifies potential logic bugs
Suggests constructive refactoring
3. Orchestration Flow
async def review_pr(pr_id):
# 1. Carregar contexto
context = load_context(
global=["conventions", "style_guide"],
task=["pr_diff", "related_files"]
)
# 2. Executar skills em paralelo
results = await parallel(
security_check(context),
style_review(context),
logic_analysis(context)
)
# 3. Consolidar e gerar sugestões
if results.has_issues():
suggestions = await suggest_improvements(
context.extend(issues=results.issues)
)
# 4. Formatar output com citações
return format_review(
results, suggestions,
require_citations=True
)
4. Grounding and Anti-Hallucination
Cada issue reportado deve: 1. Citar linha específica do diff: [L{line_number}] 2. Referenciar regra do style_guide se aplicável 3. Incluir snippet do código problemático Se não tiver certeza: - Use "Possível issue: {descrição}" - Não afirme bugs sem evidência no diff Nunca assuma: - Contexto não fornecido - Código fora do diff - Intenção do desenvolvedor
Advanced Level Final Project
Your ticket to the Masterclass
Challenge: Design an Agent System
Choose a domain and design a complete agent by applying all the advanced-level concepts. This project will be your foundation for the Masterclass.
Sales assistant with catalog, orders, and support
Code assistant, debugging helper, documentation generator
Data analyst with queries, visualizations, and insights
Project Requirements
Define all context layers (System, Global, Task, Data) with a clear hierarchy.
Create at least 4 specialized skills with triggers and context requirements.
Document how skills are activated and how context flows between them.
Define how the agent avoids hallucinations and keeps answers grounded in facts.
If you use dynamic data, define a RAG + Long Context strategy.
Next Step: Masterclass
In the Masterclass, you will implement your project with: