AI Agents · Page 6
Machine-readable specs and documentation designed specifically for AI parsers and bots.
72 articles · Page 6 of 6
AI Agent Content Specification
Specification for structuring web content readable by AI agents — frontmatter, body patterns, llms.txt, ai.txt, agent.md, JSON-LD, per-platform tips.
Function Calling Documentation Spec: How to Document Tools for AI Agents
Function calling documentation spec: how to describe tools, parameters, errors, and examples so AI agents can reliably invoke them in production.
The Future of AI Agents and Search
Analysis of how AI agents could reshape search, content discovery, and digital commerce over the next 2-5 years — framed as scenarios, with explicit uncertainty.
MCP Server Design for Content Publishers and Docs Teams
MCP server design patterns for content publishers: how to expose articles, search, and citation manifests to AI agents via Model Context Protocol.
MCP Server Onboarding Checklist
Ship an MCP server agents can pick up immediately: tool naming, schemas, examples, auth, and sandbox requirements in a single onboarding checklist.
MCP vs Function Calling vs OpenAI Plugins: AI Agent Tool Integration Architectures Compared
MCP vs function calling vs plugins compared for AI agent tool integration: discovery scope, maintainability, and documentation patterns for 2026 stacks.
Verified Agent Identity for Citation Trust: A Specification for Authenticated AI Crawlers
Specification for verified agent identity: how publishers authenticate AI crawlers via cryptographic signatures so citation trust survives spoofing.
What Are AI Agents?
What AI agents are, how they work, and why they matter for content strategy in 2026 — autonomous AI systems that perceive, reason, plan, and act on behalf of users.
What Is an MCP Server? Architecture and Citation Implications
An MCP server exposes tools, resources, and prompts to AI agents over a standardized protocol. Definition, architecture, comparisons, and citation implications.
What Is Multi-Agent Orchestration? Patterns, Frameworks, and Tradeoffs
Multi-agent orchestration coordinates specialized AI agents — planners, supervisors, and workers — through routing, shared state, and structured handoffs.
What Is Prompt Injection Defense for Agents?
Prompt injection defense protects agents from malicious instructions hidden in tool outputs and user input. Learn the layered defenses that actually work.
What Is Tool Calling for AI Agents? Definition, Patterns, and Best Practices
Tool calling lets AI agents invoke external functions and APIs through structured JSON schemas. Learn how it works in OpenAI, Anthropic, Gemini, and MCP.