Human-in-the-Loop AI: Designing Approval Gates That Actually Control Risk
An engineering guide to risk tiers, durable approval state, reviewer UX, audit trails, and safe execution for consequential AI agent actions.
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Tag: AI Agents
Read Human-in-the-Loop AI: Designing Approval Gates That Actually Control Risk
RAG vs. Long Context vs. Tool Use: Choosing the Right AI Knowledge Architecture
A practical framework for deciding when an AI system needs long context, retrieval-augmented generation, live tools, or a governed combination.
AI Coding Tools in 2026: A Practical Engineering Comparison
A current, workflow-focused comparison of OpenAI Codex, Claude Code, Cursor, GitHub Copilot, Google Antigravity, and Gemini Code Assist, with security and enterprise selection criteria.
MCP, Plugins, Tools, and Agents: How AI Connects to Applications
A practical explanation of APIs, tools, connectors, plugins, MCP hosts and servers, and agents, including authentication, authorization, trust boundaries, and enterprise governance.
Claude Fable 5.1: A Business Guide to Long-Running AI Work
Claude Fable 5.1 targets long-running, tool-heavy work. This guide explains its capabilities, cache economics, migration limits, safeguards, retention, and a measured adoption plan.
GPT-6 Astra for Business: A Practical Adoption Guide
GPT-6 Astra brings long-horizon agent features, a 1.05 million-token context window, staged availability, and premium pricing. This guide shows business leaders how to evaluate it with realistic workloads, cost measures, and governance controls.
How to Secure AI Agents Before They Access Real Systems
A broad production security baseline for AI agents covering identity, least privilege, tool policy, data boundaries, secrets, approvals, sandboxing, auditability, and kill switches.
Building a Production AI Agent on AWS with Amazon Bedrock
A production-focused Amazon Bedrock agent architecture covering model access, IAM, tools, Lambda, data, secrets, approvals, observability, reliability, and operations.
AI Agents vs. Traditional Automation: A Practical Decision Guide
A practical framework for deciding when deterministic automation is enough, where an AI-assisted workflow adds value, and when an autonomous agent is justified.