This aggregates CodeNodeX AI community articles and discussions related to AI Agent, covering concept explanations, tool comparisons, engineering practices, common issues, and indie hacking experience.
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Model Context Protocol (MCP) is Anthropic's "USB-C interface for AI applications." This article explains the host/client/server model, resources/tools/prompts, and JSON-RPC, then builds a TypeScript MCP Server for Claude Code from scratch.
"Agent ≠ multi-turn dialogue." Breaks down the four components of an AI Agent—planning, memory, tools, execution—and explains ReAct vs. Plan-Execute, short/long-term memory design, function calling engineering points, and pushing an Agent from demo to production via loop control, error recovery, and observability.
After a user confirms a high-risk action once, repeated clicks, queue re-sends, and parameter substitution can all reuse the approval. This article closes those windows with a canonical action digest, one-time consumption, state versions, and idempotent execution.
Server-side tests passing does not mean consumers still work. This article combines specification diffs, consumer contracts, cross-version replay, and migration windows into compatibility gates to constrain Agent modifications to REST, events, and message structures.
The most common problem in multi-agent systems isn't model capability, but incomplete inputs, unclear responsibility, and unverifiable results after task delegation. This article covers delegation contracts, result schemas, budget propagation, and recovery mechanisms.
Letting agents freely fetch URLs turns the model into an intranet request proxy. This article covers URL normalization, full-address validation, connection IP pinning, per-hop redirect inspection, egress proxies, and an attack regression suite.
An agent's evaluation set determines what the team will optimize for. This article covers how to sample from real workflows, annotate observable outcomes, cover failure paths, and maintain versions, so evaluation results actually guide engineering decisions.
Technical trends, engineering practices, and indie-developer perspectives around AI, AI Agents, and Vibe Coding.
Technical trends, engineering practices, and an independent developer perspective on MCP and AI Agents.
Technical trends, engineering practices, and independent developer perspectives around AI Agents and large language models.
An independent developer's perspective on technical trends and engineering practices involving RAG and AI agents.
Technical trends, engineering practices, and an independent-developer perspective around AI and AI agents.