Category: Agentic Design Patterns
Why System Prompts Aren’t Security Boundaries: Moving to Zero-Trust AI Agent Architectures
When autonomous agents move from text generation to mutating production state, executing dynamic code, modifying ledgers, or triggering payouts, traditional perimeter firewalls and system prompts fall short. System prompts are soft constraints easily bypassed by prompt injection or model drift. To run agents safely in production, we must enforce deterministic security controls outside the LLM…
Read MoreAI Stack: Products & Software
The AI Stack: Products & Software reference maps Well-Architected pillars to modern AWS, Azure, and open-source technologies used to build production AI agents with MCP, RAG, and LLM systems. It compares ingestion, transformation, retrieval, inference, evaluation, and observability layers to help teams choose managed vs open architectures. Pillar Capability AWS Azure Open Source Operational Excellence…
Read MoreAgentic Pattern – Reflection notebook code
code: https://github.com/krishnamanchikalapudi/examples.ipynb/blob/develop/LLM/Agentic%20Patterns/Parallelization.ipynb
Read MoreAgentic Pattern – Parallelization notebook code
code: https://github.com/krishnamanchikalapudi/examples.ipynb/blob/develop/LLM/Agentic%20Patterns/Parallelization.ipynb
Read MoreAgentic Pattern – Routing notebook code
Code: https://github.com/krishnamanchikalapudi/examples.ipynb/blob/develop/LLM/Agentic%20Patterns/Routing.ipynb
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