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11 docs tagged with "agents"

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Agent Security & Sandboxing

How to secure tool-using LLM agents with threat modeling, prompt-injection defenses, sandboxing, egress controls, least-privilege tools, approval gates, and audit trails.

Agent Skills

Portable, on-demand workflow packages for coding agents - what they are, how they differ from rules and project memory, how to use them across tools, and how to author your own with examples.

AI Agent Harnesses

The AI coding-agent harnesses I run in the terminal -- opencode, pi, and herdr -- what a harness is, how they differ, and how I use them day to day.

AI Agents

What makes an LLM system an agent, how tool use works, the canonical multi-agent patterns, and the MCP and A2A protocols that connect agents to tools and to each other.

AI-Assisted Software Development

Using LLM coding agents inside an engineering workflow - the alignment-before-generation methodologies (SPDD, architect-as-orchestrator), architecture patterns that suit AI (deep modules, vertical slices), evidence on productivity, and the economics driving adoption.

Context & Prompt Engineering

The context window as a finite budget, why prompt engineering grew into context engineering, context rot, and the long-horizon techniques - compaction, structured note-taking, sub-agents, and just-in-time retrieval.

Human-in-the-Loop

Approval gates, escalation, and accountability patterns for agents and LLM features that act in the real world - maker-checker, confidence thresholds, and audit trails.

MCP & A2A in Production

Production notes for Model Context Protocol and Agent2Agent - architecture, transports, authorization, security, registries, observability, and adoption trade-offs.

Project Memory & Rules

Always-on context for coding agents - AGENTS.md, CLAUDE.md, GitHub Copilot instructions, Cursor rules, Gemini context, Windsurf rules, and how to split conventions from on-demand skills.

Structured Outputs

Getting reliable JSON and schema-bound responses from LLMs - native structured output modes, validation and repair loops, and when structure beats free-form prose.

Tooling and Frameworks

A map of the AI application tooling landscape -- orchestration frameworks, connectivity protocols, vector databases, evaluation and observability, and the LLMOps discipline that ties them together.