A design handbook for architects creating and deploying advanced goal-driven AI solutions.
Agents vary widely in design, shaping their abilities—from basic rules to advanced, adaptive learning systems.
Retrieval-Augmented Generation (RAG) links LLMs to real-time, trusted data, reducing hallucinations and delivering accuracy fit for enterprises.
External data is converted into vector embeddings.
User query retrieves relevant chunks from the vector database.
Retrieved context is added to the original prompt.
LLM generates a grounded, factual response.
Selecting a reasoning framework is a crucial choice, balancing flexibility and performance.
A cyclic `Think → Act → Observe` process. Flexible and well-suited for dynamic tasks, though it may be slower and more resource-intensive.
Best For: Web research, debugging.
A planner designs a complete strategy in advance for an executor to implement. Efficient and cost-effective for organized tasks, yet less adaptable.
Best For: Report generation, data analysis.
From free frameworks to robust cloud solutions, explore the tools for creating and deploying agentic AI.
A review of top frameworks focusing on their usability and community support.
Comparing AWS, Azure, and GCP for enterprise AI deployment in critical strategic domains.