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#AI Agent

Collection

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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@admin·10 min read·Agent Engineering

State Management for Multi-Turn Conversations

Single-turn conversations are easy; multi-turn conversations are hard. Users follow up, change their minds, refer to prior context, and interrupt. This article covers context management, dialogue state, coreference resolution, topic switching, interruption, and resumption so conversations remain coherent.

#AI Agent #TypeScript♥ 840 comments
@admin·11 min read·Agent Engineering

Automating Agent Evaluation

Agents are non-deterministic, multi-step, and call tools — manual testing can't keep up. This article explains how to automate agent evaluation: build task sets + rubrics, batch-score with LLM-as-judge, do trace-driven regression, wire eval into CI, and mine bad cases from production traces for auto-intake — so every agent change gets an objective score.

#AI Agent #DevOps♥ 920 comments
@admin·8 min read·Agent Engineering

Security Auditing for MCP Servers

MCP lets AI tools plug into your capabilities, but also exposes data and operations to every compatible client. This guide covers least-privilege tools, validation and injection defenses, remote authentication and rate limiting, local-vs.-remote trust boundaries, third-party supply-chain risk, and a prelaunch checklist.

#AI Agent #DevOps♥ 940 comments
@admin·11 min read·Agent Engineering

Agent Loop Design: Stopping, Reflection, Branching

An Agent is a loop at its core, but whether that loop is well-designed decides if it is intelligent or just thrashing. This article covers the key design points of the Agent loop: stop conditions (when to end), self-reflection (self-assess after each step), branching and backtracking, max steps and budgets, and adding "human checkpoints" to intercept

#AI #AI Agent♥ 990 comments
@admin·9 min read·Agent Engineering

Red-Teaming LLM Applications: Proactively Finding Vulnerabilities

Attack before launch, or users will afterward. LLM red teaming finds prompt injection, jailbreaks, privilege abuse, data exfiltration, harmful output, and denial of service. This article covers adversarial prompt sets, LLM-generated attacks, and turning findings into regression tests so you discover flaws before users do.

#AI Agent #DevOps♥ 1020 comments
@admin·12 min read·Agent Engineering

Communication Design for Multi-Agent Systems

With multiple Agents, the hardest problem is not individual capability but communication: shared state or message passing, protocols, asynchrony, and errors. This article explains blackboards, message passing, handoffs, serialization and contracts, supervisor/pub-sub/peer-to-peer architectures, plus engineering concerns around serialization, versioning, and

#AI #AI Agent♥ 1090 comments