Observed Signal · May 23, 2026 · Article Publication · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
AI Agents in Practice — Series Overview
A DEV Community article by Gursharan Singh (published 2026-05-23) presents an actively maintained, vendor-neutral series called “AI Agents in Practice.” The series focuses on building production-grade AI agents from first principles — explaining why prototype demos fail in production, what qualifies as an agent (a control loop with tools, state, and boundaries), and the core primitives (MCP for acting, RAG for knowledge, and reusable Skills). Part 1 and Part 2 are linked; Part 3 on agent execution loops is forthcoming. The post positions the series as practical and production-oriented, emphasizing patterns, engineering constraints (state, context, stopping conditions), and integrations with tool-calling and retrieval pipelines.
Technical, vendor-neutral guide on production-grade AI agents offers engineering patterns (MCP, RAG, Skills) that are relevant to teams building agentic infrastructure; informative but not a major platform release or industry-shifting announcement.
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Key Takeaways & Evidence Grounding
- Gursharan Singh published “AI Agents in Practice — Read from the beginning” on DEV Community on 2026-05-23.
- The series is described as a practical, production-oriented guide to building AI agents and is actively maintained with new parts to be linked as published.
- Part 1 is titled “The Demo Worked. Production Didn't.” and Part 2 is titled “What Makes Something an Agent.” Part 3 (How the Loop Actually Works) is listed as forthcoming.
- The author defines an agent in engineering terms as a control loop with tools, state, and boundaries and identifies three primitives: MCP (for acting), RAG (for knowing), and Skills (reusable procedures).
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