Ai Agents Roadmap

Comprehensive, production-grade learning path for Ai Agents, architected with foundational-to-advanced pedagogical progression.

Phase 1 Phase 0: Orientation & Mental Models

Establishes the foundational vocabulary and ecosystem context for AI Agents. This phase clarifies the distinction between standard LLM interactions and agentic workflows, setting the stage for technical implementation.

Milestone What are AI Agents?: Agent Context and Tool Interchange Standard

Standardized protocol for identifying and providing data context and execution tools to autonomous AI agents, independent of specific platforms.

Milestone What are AI Agents?: Autonomous Planning and Execution Loops

Modeling evolution and population genetics using genetic algorithms and computational biology.

Milestone Fine-tuning vs Prompt Engineering: Prompt Engineering

Designing effective prompts for large language models: system prompts, few-shot examples, chain-of-thought, tuning temperature/top-p, prompt templates, and prompt quality evaluation.

Milestone Fine-tuning vs Prompt Engineering: Large Language Model Principles

Fundamental principles of large language models, including pre-training, instruction tuning, and fine-tuning.

Milestone Example Usecases: Generative Content Application

Building Generative AI applications: pipeline from user input through prompt to verified output, generating content for specific domains (multiple-choice questions, summarization, translation).

Milestone Example Usecases: AI-Assisted Design Tool

A design tool or feature integrating AI to automate the generation of layouts, interface variants, and interaction states from text descriptions. Using this tool accelerates prototyping and reduces repetitive manual tasks.

Phase 2 Phase 1: Foundations & First API Call

Focuses on the immediate prerequisites for interacting with LLMs via code. Learners set up their development environment, understand how models process text (tokenization), and make their first functional API call to generate text.

Milestone Git and Terminal Usage: Version Control Workflow

Understand and apply a Git workflow, such as Git Flow, to manage branches (feature, develop, release) effectively within a team.

Milestone Git and Terminal Usage: Iterative Development Practices

Master the core principles of agile software development, such as prioritizing customer satisfaction and adapting to change.

Milestone Tokenization: Input Processing and Limits

Integrating large language models via API: authentication, request/response lifecycle, token limits, rate limiting, retry/backoff, streaming responses, and error handling.

📚 Prerequisites: LARGE_LANGUAGE_MODEL_CONCEPTS, PROMPT_ENGINEERING

Phase 3 Phase 2: Prompt Engineering & Reasoning Patterns

Teaches how to control LLM output effectively through structured prompting. Introduces reasoning techniques (Chain of Thought, Tree of Thought) that enable agents to solve complex problems step-by-step rather than guessing.

Milestone Chain of Thought (CoT): Sequential Reasoning Steps

Technique of breaking a large, complex problem into smaller, more manageable subproblems, a core part of computational thinking.

Milestone Chain of Thought (CoT): Probabilistic Logic Validation

Analysis of the reasoning differences between deterministic rule-based systems and probabilistic data-driven systems (Data-Driven / Explainable AI).

Milestone Tree-of-Thought: Structural Similarity Analysis

Ability to detect similarities, trends, or recurring patterns across problems to reuse known solutions.

Phase 4 Phase 3: Tool Use & The Agent Loop

Introduces the core mechanism of agents: the ability to use external tools. Covers defining tools, invoking them via APIs, and understanding the ReAct (Reason + Act) loop where the agent observes results and decides next steps.

Milestone Tool Definition: Agent Tool Use and Function Calling

The learner will be able to enable an AI agent to call external tools and functions, handle structured inputs and outputs, and incorporate results into an ongoing task following a safe and reliable tool-use loop.

Milestone Tool Definition: Tool Error Handling and Retry Logic

📚 Prerequisites: PROBABILISTIC_VS_DETERMINISTIC_REASONING

Milestone REST API Knowledge: API Integration

Understand the technique of calling external APIs to fetch data or use functionality from another service in your application.

Phase 5 Phase 4: Memory & Context Management

Addresses the limitation of stateless LLMs by implementing short-term and long-term memory. Covers RAG (Retrieval-Augmented Generation), vector embeddings, and managing conversation history to maintain context over time.

Milestone Embeddings and Vector Search: Hybrid Vector and Sparse Keyword Search

Method combining vector-space semantic retrieval with sparse keyword search (BM25/sparse) to improve knowledge retrieval accuracy.

Milestone Short Term Memory: Local View State

Using @State to manage simple state local to a single view, destroyed when the view disappears.

Milestone Episodic vs Semantic Memory: Durable Agent State Management

Mechanism for maintaining and separating structured state (task status, milestones) and unstructured state (rationale logs), enabling agents to recover from interruptions.

📚 Prerequisites: AGENT_TOOL_USE_FUNCTION_CALLING

Phase 6 Phase 5: Advanced Architectures & MCP & Production, Evaluation & Governance

Explores standardized protocols for tool integration (Model Context Protocol - MCP) and advanced planning architectures. Teaches how to create reusable MCP servers and structure complex multi-step plans. Covers the final mile: making agents reliable, observable, and safe. Includes evaluation metrics, testing strategies, handling open/closed weight models, and addressing ethical considerations after hands-on experience.

Milestone Model Context Protocol (MCP): Hypertext Transfer Protocol

The learner will be able to explain the HTTP protocol's request/response cycle, methods, status codes, headers, and its role in web communication.

Milestone Evaluation and Testing: Unit Testing

Understand and write unit tests to verify the correctness of each function or method independently.

Milestone Evaluation and Testing: AI Artifact Verification & Auditing

Principles of verification, critical evaluation, and validation of correctness and safety of code/data artifacts generated by AI systems.

Milestone Open Weight Models: Cloud Service Models

Distinguish the three main cloud computing service models: IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service).