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Agentic & Autonomous AI A cutting-edge roadmap for building autonomous AI agents. This path covers the mathematical foundations, machine learning, deep learning, large language models, prompt engineering, agent frameworks, tool use, multi-agent systems, memory management, autonomous reasoning, safety, and production deployment of AI agents.
12 milestones in this roadmap
Step 1 beginner 6-8 weeks
Python & Mathematics Foundation Build a strong foundation in Python programming and the mathematics (linear algebra, calculus, probability) that underpins all of AI.
1 Python fundamentals: data structures, functions, OOP, and type hints 2 NumPy for vectorised computation and matrix operations 3 Linear algebra: vectors, matrices, eigenvalues, and singular value decomposition 4 Multivariable calculus: gradients, chain rule, and optimisation (gradient descent) 5 Probability distributions, Bayes theorem, and maximum likelihood estimation 6 Pandas and Matplotlib for data manipulation and visualisation
Python 3 / Jupyter Notebooks NumPy / Pandas / Matplotlib SymPy for symbolic math Google Colab
Step 1 beginner 6-8 weeks
Python & Mathematics Foundation Build a strong foundation in Python programming and the mathematics (linear algebra, calculus, probability) that underpins all of AI.
1 Python fundamentals: data structures, functions, OOP, and type hints 2 NumPy for vectorised computation and matrix operations 3 Linear algebra: vectors, matrices, eigenvalues, and singular value decomposition 4 Multivariable calculus: gradients, chain rule, and optimisation (gradient descent) 5
Step 2 beginner 6-8 weeks
Machine Learning Fundamentals Master classical machine learning algorithms, model evaluation, and the bias-variance tradeoff with hands-on Scikit-learn projects.
1 Supervised learning: linear regression, logistic regression, decision trees, random forests, SVMs 2 Unsupervised learning: k-means clustering, hierarchical clustering, PCA, and DBSCAN 3 Model evaluation: cross-validation, precision, recall, F1, ROC-AUC, and confusion matrices 4 Bias-variance tradeoff, overfitting, regularisation (L1/L2), and ensemble methods
Step 3 intermediate 8-10 weeks
Deep Learning (PyTorch & TensorFlow) Build and train neural networks with PyTorch covering CNNs, RNNs, transfer learning, and GPU-accelerated training.
1 Perceptrons, activation functions, and universal approximation theorem 2 Backpropagation, loss functions (cross-entropy, MSE), and gradient flow 3 Optimisers: SGD, Adam, AdaGrad, learning rate schedules and warm-up 4 CNNs for image classification: convolutions, pooling, ResNet, EfficientNet
Step 4 intermediate 6-8 weeks
NLP & Large Language Models Understand natural language processing from tokenisation through the Transformer architecture and how modern LLMs are trained.
1 Tokenisation (BPE, WordPiece, SentencePiece) and embedding representations 2 Word2Vec, GloVe, and contextual embeddings (ELMo) 3 Transformer architecture: self-attention, multi-head attention, positional encoding 4 Pre-training objectives: masked language modelling, causal language modelling
Step 5 intermediate 4-6 weeks
Prompt Engineering & RAG Master prompt engineering techniques and build RAG pipelines that ground LLM responses in factual, domain-specific knowledge.
1 Zero-shot, few-shot, chain-of-thought, and tree-of-thought prompting 2 System prompts, role prompting, and structured output (JSON mode) 3 Document chunking strategies: fixed-size, semantic, recursive, and parent-child 4 Embedding models, vector similarity search, and re-ranking
Step 6 intermediate 6-8 weeks
AI Agent Frameworks (LangChain/CrewAI/AutoGen) Build AI agents with popular frameworks using ReAct loops, structured outputs, and composable chain and graph workflows.
1 Agent loop patterns: ReAct (reason + act), Plan-and-Execute, and reflection 2 LangChain: chains, agents, output parsers, and callback handlers 3 LangGraph: state machines, conditional edges, and human-in-the-loop nodes 4 CrewAI: role-based agents, tasks, tools, and collaboration patterns
Step 7 intermediate 4-6 weeks
Tool Use & Function Calling Enable agents to interact with external systems through function calling, API integration, code execution, and browser control.
1 Function calling: JSON schema definitions, parameter validation, and error handling 2 Web browsing agents: HTTP requests, HTML parsing, and headless browser control 3 Database query agents: natural language to SQL, result interpretation 4 Code execution sandboxing: Docker containers, E2B, and security boundaries
Step 8 advanced 4-6 weeks
Multi-Agent Systems Design systems where multiple specialised agents collaborate using orchestration patterns to solve complex problems.
1 Orchestration patterns: hierarchical supervisor, peer-to-peer, and debate 2 Agent handoffs, shared context, and message passing protocols 3 Specialised agent roles: researcher, coder, reviewer, planner 4 Conflict resolution and consensus mechanisms between agents
Step 9 advanced 4-6 weeks
Memory & State Management Implement short-term, long-term, and episodic memory systems that make agents contextually aware across sessions.
1 Conversation history management: sliding window, summarisation, and compression 2 Long-term memory with vector stores and knowledge graph retrieval 3 Episodic memory: storing and retrieving past task outcomes and lessons 4 Working memory: scratchpads, chain-of-thought buffers, and context assembly
Step 10 advanced 4-6 weeks
Autonomous Decision Making Build agents that plan, reason, and self-correct autonomously over extended tasks using goal decomposition and reflection.
1 Task decomposition: hierarchical planning and sub-goal generation 2 Self-reflection loops: critique, revision, and iterative improvement 3 Monte Carlo Tree Search and beam search for planning under uncertainty 4 Reward modelling, heuristic evaluation, and action quality assessment
Step 11 advanced 3-4 weeks
Safety & Alignment Understand AI safety risks and implement guardrails, content filtering, and alignment techniques for responsible agent deployment.
1 Prompt injection attacks: direct injection, indirect injection, and defences 2 Hallucination detection, grounding, and factuality verification 3 Output validation, content filtering, and toxicity detection 4 Guardrail frameworks: input/output validators, topic restrictions, PII detection
Step 12 advanced 4-6 weeks
Production Deployment of AI Agents Deploy AI agents to production with proper serving infrastructure, observability, cost tracking, and evaluation pipelines.
1 API serving with FastAPI, streaming responses, and WebSocket support 2 Model hosting: vLLM, TGI, Ollama for self-hosted inference 3 Observability: tracing agent runs, token usage, latency, and error rates 4 Cost management: prompt caching, model routing, and token budgets Ready to start this journey? Browse our courses and books to begin your learning path.
Probability distributions, Bayes theorem, and maximum likelihood estimation
6 Pandas and Matplotlib for data manipulation and visualisation
Python 3 / Jupyter Notebooks NumPy / Pandas / Matplotlib SymPy for symbolic math Google Colab
5 Feature engineering: encoding, scaling, selection, and dimensionality reduction
6 Hyperparameter tuning: grid search, random search, and Bayesian optimisation
Scikit-learn XGBoost / LightGBM Weights & Biases / MLflow Kaggle datasets
Step 2 beginner 6-8 weeks
Machine Learning Fundamentals Master classical machine learning algorithms, model evaluation, and the bias-variance tradeoff with hands-on Scikit-learn projects.
1 Supervised learning: linear regression, logistic regression, decision trees, random forests, SVMs 2 Unsupervised learning: k-means clustering, hierarchical clustering, PCA, and DBSCAN 3 Model evaluation: cross-validation, precision, recall, F1, ROC-AUC, and confusion matrices 4 Bias-variance tradeoff, overfitting, regularisation (L1/L2), and ensemble methods 5 Feature engineering: encoding, scaling, selection, and dimensionality reduction 6 Hyperparameter tuning: grid search, random search, and Bayesian optimisation
Scikit-learn XGBoost / LightGBM Weights & Biases / MLflow Kaggle datasets
5
RNNs, LSTMs, and GRUs for sequence modelling
6 Transfer learning, fine-tuning, regularisation (dropout, batch norm), and data augmentation
PyTorch TensorFlow / Keras CUDA / cuDNN Weights & Biases
Step 3 intermediate 8-10 weeks
Deep Learning (PyTorch & TensorFlow) Build and train neural networks with PyTorch covering CNNs, RNNs, transfer learning, and GPU-accelerated training.
1 Perceptrons, activation functions, and universal approximation theorem 2 Backpropagation, loss functions (cross-entropy, MSE), and gradient flow 3 Optimisers: SGD, Adam, AdaGrad, learning rate schedules and warm-up 4 CNNs for image classification: convolutions, pooling, ResNet, EfficientNet 5 RNNs, LSTMs, and GRUs for sequence modelling 6 Transfer learning, fine-tuning, regularisation (dropout, batch norm), and data augmentation
PyTorch TensorFlow / Keras CUDA / cuDNN Weights & Biases
5
Fine-tuning techniques: LoRA, QLoRA, prefix tuning, and adapter layers
6 RLHF, DPO, and instruction tuning for alignment
Hugging Face Transformers OpenAI API / Anthropic API Ollama / vLLM for local inference Hugging Face Datasets & Tokenizers
Step 4 intermediate 6-8 weeks
NLP & Large Language Models Understand natural language processing from tokenisation through the Transformer architecture and how modern LLMs are trained.
1 Tokenisation (BPE, WordPiece, SentencePiece) and embedding representations 2 Word2Vec, GloVe, and contextual embeddings (ELMo) 3 Transformer architecture: self-attention, multi-head attention, positional encoding 4 Pre-training objectives: masked language modelling, causal language modelling 5 Fine-tuning techniques: LoRA, QLoRA, prefix tuning, and adapter layers 6 RLHF, DPO, and instruction tuning for alignment
Hugging Face Transformers OpenAI API / Anthropic API Ollama / vLLM for local inference Hugging Face Datasets & Tokenizers
5
Vector databases: indexing (HNSW, IVF), filtering, and hybrid search
6 RAG evaluation: faithfulness, relevance, and answer correctness metrics
Pinecone / Weaviate / Chroma / Qdrant LangChain / LlamaIndex OpenAI Embeddings / Cohere Embed Ragas / DeepEval for evaluation
Step 5 intermediate 4-6 weeks
Prompt Engineering & RAG Master prompt engineering techniques and build RAG pipelines that ground LLM responses in factual, domain-specific knowledge.
1 Zero-shot, few-shot, chain-of-thought, and tree-of-thought prompting 2 System prompts, role prompting, and structured output (JSON mode) 3 Document chunking strategies: fixed-size, semantic, recursive, and parent-child 4 Embedding models, vector similarity search, and re-ranking 5 Vector databases: indexing (HNSW, IVF), filtering, and hybrid search 6 RAG evaluation: faithfulness, relevance, and answer correctness metrics
Pinecone / Weaviate / Chroma / Qdrant LangChain / LlamaIndex OpenAI Embeddings / Cohere Embed Ragas / DeepEval for evaluation
5 AutoGen: conversational agents, group chat, and function calling
6 Structured output parsing, retry logic, and fallback strategies
LangChain / LangGraph CrewAI AutoGen / AG2 Claude Code / OpenAI Assistants API
Step 6 intermediate 6-8 weeks
AI Agent Frameworks (LangChain/CrewAI/AutoGen) Build AI agents with popular frameworks using ReAct loops, structured outputs, and composable chain and graph workflows.
1 Agent loop patterns: ReAct (reason + act), Plan-and-Execute, and reflection 2 LangChain: chains, agents, output parsers, and callback handlers 3 LangGraph: state machines, conditional edges, and human-in-the-loop nodes 4 CrewAI: role-based agents, tasks, tools, and collaboration patterns 5 AutoGen: conversational agents, group chat, and function calling 6 Structured output parsing, retry logic, and fallback strategies
LangChain / LangGraph CrewAI AutoGen / AG2 Claude Code / OpenAI Assistants API
5 File system operations, document processing, and multi-modal inputs
6 Tool selection strategies: dynamic tool loading and tool description optimisation
OpenAI Function Calling / Claude Tool Use Playwright / Selenium for browser automation E2B / Modal for sandboxed execution Composio / Toolhouse
Step 7 intermediate 4-6 weeks
Tool Use & Function Calling Enable agents to interact with external systems through function calling, API integration, code execution, and browser control.
1 Function calling: JSON schema definitions, parameter validation, and error handling 2 Web browsing agents: HTTP requests, HTML parsing, and headless browser control 3 Database query agents: natural language to SQL, result interpretation 4 Code execution sandboxing: Docker containers, E2B, and security boundaries 5 File system operations, document processing, and multi-modal inputs 6 Tool selection strategies: dynamic tool loading and tool description optimisation
OpenAI Function Calling / Claude Tool Use Playwright / Selenium for browser automation E2B / Modal for sandboxed execution Composio / Toolhouse
5
Communication protocols: synchronous, asynchronous, and event-driven
6 Evaluating multi-agent vs single-agent performance and cost trade-offs
CrewAI (multi-agent) LangGraph (multi-agent graphs) AutoGen (group chat) Swarm / OpenAI Agents SDK
Step 8 advanced 4-6 weeks
Multi-Agent Systems Design systems where multiple specialised agents collaborate using orchestration patterns to solve complex problems.
1 Orchestration patterns: hierarchical supervisor, peer-to-peer, and debate 2 Agent handoffs, shared context, and message passing protocols 3 Specialised agent roles: researcher, coder, reviewer, planner 4 Conflict resolution and consensus mechanisms between agents 5 Communication protocols: synchronous, asynchronous, and event-driven 6 Evaluating multi-agent vs single-agent performance and cost trade-offs
CrewAI (multi-agent) LangGraph (multi-agent graphs) AutoGen (group chat) Swarm / OpenAI Agents SDK
5
Memory indexing, relevance scoring, and forgetting strategies
6 Persistent state management across sessions and agent restarts
Chroma / Weaviate / Pinecone Neo4j / MemGraph for knowledge graphs LangGraph checkpointing Redis for session state
Step 9 advanced 4-6 weeks
Memory & State Management Implement short-term, long-term, and episodic memory systems that make agents contextually aware across sessions.
1 Conversation history management: sliding window, summarisation, and compression 2 Long-term memory with vector stores and knowledge graph retrieval 3 Episodic memory: storing and retrieving past task outcomes and lessons 4 Working memory: scratchpads, chain-of-thought buffers, and context assembly 5 Memory indexing, relevance scoring, and forgetting strategies 6 Persistent state management across sessions and agent restarts
Chroma / Weaviate / Pinecone Neo4j / MemGraph for knowledge graphs LangGraph checkpointing Redis for session state
5
Autonomous error recovery, replanning, and adaptive strategies
6 Human-in-the-loop escalation policies and confidence thresholds
LangGraph (planning graphs) AutoGPT / BabyAGI patterns Tree of Thoughts implementations Anthropic Claude (extended thinking)
Step 10 advanced 4-6 weeks
Autonomous Decision Making Build agents that plan, reason, and self-correct autonomously over extended tasks using goal decomposition and reflection.
1 Task decomposition: hierarchical planning and sub-goal generation 2 Self-reflection loops: critique, revision, and iterative improvement 3 Monte Carlo Tree Search and beam search for planning under uncertainty 4 Reward modelling, heuristic evaluation, and action quality assessment 5 Autonomous error recovery, replanning, and adaptive strategies 6 Human-in-the-loop escalation policies and confidence thresholds
LangGraph (planning graphs) AutoGPT / BabyAGI patterns Tree of Thoughts implementations Anthropic Claude (extended thinking)
5
Red-teaming, adversarial testing, and jailbreak prevention
6 AI alignment principles: helpful, harmless, honest, and constitutional AI
Guardrails AI / NeMo Guardrails Anthropic Constitutional AI principles Lakera Guard / Rebuff OWASP LLM Top 10
Step 11 advanced 3-4 weeks
Safety & Alignment Understand AI safety risks and implement guardrails, content filtering, and alignment techniques for responsible agent deployment.
1 Prompt injection attacks: direct injection, indirect injection, and defences 2 Hallucination detection, grounding, and factuality verification 3 Output validation, content filtering, and toxicity detection 4 Guardrail frameworks: input/output validators, topic restrictions, PII detection 5 Red-teaming, adversarial testing, and jailbreak prevention 6 AI alignment principles: helpful, harmless, honest, and constitutional AI
Guardrails AI / NeMo Guardrails Anthropic Constitutional AI principles Lakera Guard / Rebuff OWASP LLM Top 10
5
Evaluation pipelines: automated benchmarks, regression testing, and A/B testing
6 Semantic caching, rate limiting, and graceful degradation strategies
FastAPI / LitServe LangSmith / Langfuse / Arize Phoenix vLLM / Ollama Docker / Kubernetes for deployment
Step 12 advanced 4-6 weeks
Production Deployment of AI Agents Deploy AI agents to production with proper serving infrastructure, observability, cost tracking, and evaluation pipelines.
1 API serving with FastAPI, streaming responses, and WebSocket support 2 Model hosting: vLLM, TGI, Ollama for self-hosted inference 3 Observability: tracing agent runs, token usage, latency, and error rates 4 Cost management: prompt caching, model routing, and token budgets 5 Evaluation pipelines: automated benchmarks, regression testing, and A/B testing 6 Semantic caching, rate limiting, and graceful degradation strategies
FastAPI / LitServe LangSmith / Langfuse / Arize Phoenix vLLM / Ollama Docker / Kubernetes for deployment