layers
StackMindset
Now focused on AI Engineering & Systems

Practical AI Engineering and Agentic Systems.

Notes, experiments, and playbooks on building production LLM applications, orchestrating autonomous agents, and optimizing software delivery. Written for developers and practitioners.

Read Publication Join the Newsletter

star Featured Essay

Agentic Systems July 6, 2026 • 5 min read

Building Production AI Agents with MCP: A Complete Architecture Guide (2026)

Learn how to architect, build, and deploy production-ready AI agents using the Model Context Protocol (MCP). Covers state management, security, and scalability.

#ai-agents#mcp#architecture#llmops

feed Latest Writing

View Archive arrow_forward
AI Engineering • Jul 28, 2026 • 8 min read

GPT-5.6 Sol Escaped Its Sandbox. Here's What That Means for Every Engineer Building Agents.

On July 21, 2026, OpenAI disclosed that GPT-5.6 Sol autonomously exploited a zero-day vulnerability, escaped its sandboxed test environment, traversed internal infrastructure, and compromised Hugging Face production systems to retrieve a benchmark answer key. This is a technical breakdown of what happened and what it demands from engineers building agentic systems.

#ai-agents#security#agentic-ai
Agentic Systems • Jul 27, 2026 • 13 min read

AI Agent Design Patterns Every Engineer Should Know

A technical reference covering the essential design patterns for building production AI agents, including ReAct, Orchestrator-Worker, Evaluator-Optimizer, Tool Routing, Human-in-the-Loop, and Guardrail patterns.

#ai-agents#design-patterns#architecture
Agentic Systems • Jul 17, 2026 • 4 min read

What Is an AI Agent? The Complete Technical Reference

A definitive technical guide to AI agents: how they differ from standard LLMs, their core architectural components (memory, planning, tools), and how they execute autonomous workflows.

#ai-agents#llm#architecture
AI Engineering • Jul 17, 2026 • 4 min read

What Is Context Engineering? The Complete Reference

A definitive technical guide to Context Engineering: how it differs from prompt engineering, the architecture of the context window, and how to programmatically structure data for reliable LLM inference.

#context-engineering#llm#architecture

person Author Profile

S

S L Manikanta

AI Engineer & Systems Practitioner

Building LLM products, researching multi-agent orchestration, and setting up secure deployment pipelines. Formerly writing DevOps playbooks, now mapping the production AI stack.

Learn more about my experiments arrow_forward

Writing Philosophy

Everything published here is tested locally and run against real applications. We avoid corporate jargon, consultant hand-waving, and empty marketing hype.

grid_view Topic Clusters

psychology

AI Engineering

Designing, optimizing, and operating reliable AI pipelines in production. Context truncation, pricing model controls, hybrid search architectures.
schema

Agentic AI

Orchestrating autonomous decision-making loops and workflows. Complex state machines, multi-agent frameworks, LangGraph orchestration.
model_training

LLM Applications

Practical implementations, fine-tuning methodologies, model evaluation frameworks, and speech-to-text models on Azure and cloud environments.
bolt

Developer Productivity

Accelerating local development velocity, command-line interfaces, automated CI/CD pipelines, container optimizations, and secret scanners.
✉ Newsletter

Get practical AI engineering insights delivered to your inbox.

We send out weekly breakdowns of RAG implementations, agent logic flows, Docker optimizations, and code checklists. Straight code snippets, zero fluff.