
Guardrails for Production AI Agents: A Practical Guide
Real guardrails for AI agents in production: input validation, action allow-lists, sandboxing, cost ceilings, and human-in-the-loop. Patterns you can ship.
ReadInsights on AI agents, model routing, and building production-ready AI systems.

Real guardrails for AI agents in production: input validation, action allow-lists, sandboxing, cost ceilings, and human-in-the-loop. Patterns you can ship.
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Qwen 3.6 is Alibaba's open-source LLM that punches above its size on SWE-bench. Why a smaller, efficient model is often the smarter agent default.
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Five agent design patterns for reliable, low-cost AI systems: ReAct, Plan-and-Execute, Reflection, Router, and Tool-First, with trade-offs for each.
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Autonomous AI agents that run code and shell commands need isolation. Why sandboxes are non-negotiable in production, the isolation levels, and how to choose.
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A comparison of AI sandboxes for agent development in 2026: E2B, Modal, Daytona, and self-hosted options. Cold-start latency, isolation, and pricing.
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How to build a self-correcting AI agent using the reflection pattern and persistent memory. A runnable Python loop that critiques and fixes its own output.
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Build a custom MCP server that lets any AI agent run data analysis on your CSVs and databases. A complete, runnable TypeScript walkthrough.
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Claude Sonnet 4 vs GPT-4o for AI agents: tool-calling reliability, long-context behavior, cost, and latency. Which model to pick for which agent.
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A teardown of how OpenHands, the open-source AI coding agent, plans, edits files, and runs code in a sandbox: the event-stream and action-observation loop.
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