𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝟮𝟬𝟮𝟲
1. Foundations: Programming & Core Concepts
Start with the basics that power every AI agent.
🔹 Python fundamentals
🔹 Data structures & algorithms
🔹 APIs & HTTP basics
🔹 JSON, async programming, concurrency
2. Math & AI Essentials
Understand how intelligent systems actually learn.
🔹 Linear algebra (vectors, matrices)
🔹 Probability & statistics
🔹 Optimization & gradients
🔹 Machine learning fundamentals
3. Machine Learning Basics
Teach your agent to recognize patterns and make predictions.
🔹 Supervised vs unsupervised learning
🔹 Regression & classification
🔹 Model evaluation metrics
🔹 Scikit-learn workflows
4. Deep Learning & Transformers
Modern AI agents rely on deep neural networks.
🔹 Neural networks & backpropagation
🔹 PyTorch or TensorFlow
🔹 Embeddings & vector spaces
🔹 Transformers & attention mechanisms
🔹 Large Language Models (LLMs)
5. Understanding AI Agents
Learn what makes an agent different from a simple script.
🔹 Agent loop (Observe → Think → Act)
🔹 Goals & decision making
🔹 Planning & reasoning
🔹 Tool usage
🔹 Memory & context handling
6. Prompt Engineering
Communicate effectively with LLMs.
🔹 System vs user prompts
🔹 Few-shot prompting
🔹 Chain-of-thought reasoning
🔹 Structured outputs (JSON/function calling)
🔹 Guardrails & constraints
7. Memory Systems
Give your agent long-term intelligence.
🔹 Short-term memory (conversation history)
🔹 Long-term memory (vector databases)
🔹 Embeddings & semantic search
🔹 Retrieval-Augmented Generation (RAG)
🔹 Knowledge bases
8. Tools & Integrations
Make your agent useful in the real world.
🔹 REST APIs
🔹 Databases (Postgres, MongoDB, Redis)
🔹 File systems
🔹 Web scraping
🔹 Third-party services (payments, email, messaging)
9. Agent Frameworks
Accelerate development using modern ecosystems.
🔹 LangChain
🔹 LlamaIndex
🔹 OpenAI SDKs
🔹 CrewAI / multi-agent frameworks
🔹 AutoGen-style orchestration
10. Reasoning & Planning
Enable smarter, goal-oriented behavior.
🔹 Task decomposition
🔹 Step-by-step planning
🔹 Self-reflection & retry logic
🔹 Multi-step workflows
🔹 Tool selection strategies
11. Multi-Agent Systems
Build teams of agents that collaborate.
🔹 Role-based agents (planner, executor, reviewer)
🔹 Agent communication
🔹 Parallel task execution
🔹 Consensus & validation
🔹 Workflow orchestration
12. Testing & Evaluation
Measure reliability and performance.
🔹 Unit tests for tools
🔹 Prompt testing
🔹 Latency measurement
🔹 Cost tracking
🔹 Accuracy benchmarks
13. Deployment & Scaling
Bring your AI agent to production.
🔹 Docker containers
🔹 FastAPI/Next.js backends
🔹 Serverless or cloud hosting
🔹 Background jobs & queues
🔹 Monitoring & logging
14. Security & Safety
Build responsible and secure agents.
🔹 Input validation
🔹 Rate limiting
🔹 API key protection
🔹 Content filtering
🔹 Privacy & data protection
15. Real-World Projects
Solidify skills with practical builds.
🔹 AI chatbot assistant
🔹 Coding/debugging assistant
🔹 Document summarizer
🔹 Research agent
🔹 Task automation agent
🔹 Multi-agent business workflows
Mastering these steps will help you move from simple scripts to fully autonomous, production-grade AI agents.
Get the complete AI Agent Developer's Handbook here: https://codewithdhanian.gumroad.com/l/gfkbh
- Dhanian