A visual guide for architects on building and deploying the next generation of goal-directed AI systems.
Not all agents are created equal. Their architecture dictates their intelligence and capabilities, from simple rule-based actions to complex, adaptive learning.
Retrieval-Augmented Generation (RAG) is the core architecture that connects LLMs to live, authoritative data, preventing hallucinations and ensuring enterprise-grade accuracy.
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.
Choosing a reasoning framework is a key design decision, trading off between adaptability and efficiency.
An iterative `Thought → Action → Observation` loop. Highly adaptive and great for unpredictable tasks, but can be slower and more costly.
Best For: Web research, debugging.
A planner creates a full strategy upfront for an executor to follow. Faster and cheaper for structured tasks, but less flexible.
Best For: Report generation, data analysis.
From open-source frameworks to enterprise cloud platforms, here’s a look at the tools to build and deploy agentic AI.
A comparison of leading frameworks based on their learning curve and ecosystem size.
How AWS, Azure, and GCP stack up for enterprise AI deployment across key strategic areas.