AI Agents
5 articles · written by Niraj Kumar
An agent is a loop with tools, and almost every production problem with agents comes from that loop: it runs too long, calls the wrong tool, loses its state on restart, or takes an action nobody approved. Model quality is rarely the bottleneck.
These articles cover agent architecture from that angle — tool interface design, when a state machine beats a free-running loop, durable checkpointing so a crashed run can resume, human approval gates on destructive actions, and the specific pitfalls that show up once an agent handles real traffic.
Several posts pair agents with retrieval, since most useful agents are also RAG systems.
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How to Automate Multi-Axis PR Code Reviews using Claude Code and GitHub Actions CI/CD
Wire Claude Code into GitHub Actions to review pull requests across security, performance and testability — line-by-line, before a human opens the diff.
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How to Implement Human-in-the-Loop (HITL) Approval Gates in LangGraph with PostgreSQL Checkpointers
Build production human-in-the-loop approval gates in LangGraph with AsyncPostgresSaver, interrupt_before, and Command-based resume flows.
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Beyond Basic Chatbots: Developing Advanced AI Agents with LangGraph
Build stateful, multi-step AI agents with LangGraph — core concepts, real architectures, code examples, and the pitfalls beyond simple chatbots.
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Designing AI Agents: Tools, Patterns, and Pitfalls
Designing AI agents that hold up — single-step vs multi-agent workflows, tool-calling patterns, memory architecture, and the reliability pitfalls.
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Graph‑RAG Explained: Building Smarter AI Agents with Knowledge Graphs
How Graph-RAG combines knowledge graphs with LLMs to improve multi-hop reasoning and accuracy — with code examples and enterprise use cases.
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