14/08/2026
๐ช๐ฎ๐ป๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ ๐๐ด๐ฒ๐ป๐๐? ๐๐ฒ๐ฟ๐ฒโ๐ ๐๐ต๐ฒ ๐๐ผ๐บ๐ฝ๐น๐ฒ๐๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฅ๐ผ๐ฎ๐ฑ๐บ๐ฎ๐ฝ. ๐ค
Building AI agents is not just about writing better prompts.
It requires an understanding of programming, machine learning, language models, data, tools, memory, orchestration, deployment and evaluation.
๐๐ฒ๐ฟ๐ฒโ๐ ๐๐ต๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฝ๐ฎ๐๐ต ๐
๐งฑ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐๐ผ๐๐ป๐ฑ๐ฎ๐๐ถ๐ผ๐ป๐
Data types, control flow, logical decision-making and file handling.
๐ ๐๐ฎ๐๐ฎ & ๐ง๐ผ๐ผ๐น ๐จ๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ๐ถ๐ป๐ด
LLM types, APIs, authentication and application integrations.
๐ง ๐ ๐ & ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น๐
Machine learning fundamentals, transformers, Mixture of Experts, fine-tuning and inference.
๐ฏ ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐๐ถ๐ป๐ด & ๐ฅ๐ฒ๐ฎ๐๐ผ๐ป๐ถ๐ป๐ด
Structured prompting, task decomposition, planning, few-shot techniques and tool selection.
๐๏ธ ๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐ฎ๐๐ฎ & ๐ง๐ผ๐ผ๐น ๐๐ป๐๐ฒ๐ด๐ฟ๐ฎ๐๐ถ๐ผ๐ป
SQL, NoSQL, APIs, SaaS connectors, data extraction and pipelines.
๐ ๐ฅ๐๐, ๐ ๐ฒ๐บ๐ผ๐ฟ๐ & ๐ฅ๐ฒ๐๐ฝ๐ผ๐ป๐๐ถ๐ฏ๐น๐ฒ ๐๐
Embeddings, vector databases, retrieval pipelines, agent memory and reliable AI practices.
โ๏ธ ๐๐ด๐ฒ๐ป๐ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ & ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ๐
Agent types, design patterns, orchestration, planning and feedback loops.
๐ ๐๐ฒ๐ฝ๐น๐ผ๐๐บ๐ฒ๐ป๐, ๐ ๐๐ข๐ฝ๐ & ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด
Testing, logging, performance, latency, reliability and cost optimisation.
๐ ๐ ๐๐น๐๐ถ-๐๐ด๐ฒ๐ป๐ ๐๐ผ๐น๐น๐ฎ๐ฏ๐ผ๐ฟ๐ฎ๐๐ถ๐ผ๐ป
Specialised agents, communication patterns, handoffs and agent-to-agent protocols.
๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ ๐ถ๐ ๐ป๐ผ๐ ๐ท๐๐๐ ๐ฎ ๐๐ผ๐ผ๐น ๐๐ฝ๐ด๐ฟ๐ฎ๐ฑ๐ฒ.
๐๐ ๐ถ๐ ๐ฎ ๐๐๐๐๐ฒ๐บ๐ ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ ๐๐ต๐ถ๐ณ๐.
Which stage of this roadmap are you currently learning? ๐