The best resource pool on AI agents
Independent comparisons of the models, frameworks, coding agents and standards shaping agentic AI in 2026 — plus balanced debates on the decisions every business and engineering team faces.
Explore by category
Start with the layer you are deciding on.
Frontier Models
OpenAI GPT, Anthropic Claude, Google Gemini and open-weight models compared for agent workloads.
Read the comparison →
Agent Frameworks
LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Google ADK and Semantic Kernel side by side.
Read the comparison →Coding Agents
Claude Code, Codex, Cursor, GitHub Copilot and Gemini CLI — where each fits in a development team.
Read the comparison →Protocols & Standards
Function calling, Model Context Protocol (MCP) and Agent2Agent (A2A) explained and compared.
Read the comparison →Debates
Both sides, then a decision framework.
Quick guide: which tool for which job?
A starting point. Always test on your own cases.
| Job | Typical starting point | Why |
|---|---|---|
| Customer-facing agent | Closed frontier model (GPT, Claude or Gemini) | Tone, reliability and tool use matter most |
| High-volume classification | Smaller tier of any family, or open-weight | Cost per task dominates |
| Regulated data | Open-weight, self-hosted in your region | Data must stay under your control |
| First production workflow | Thin SDK plus your own orchestration | Fewer moving parts, easier to debug |
| Developer productivity | Terminal or IDE coding agent with review gates | Speed gains need human accountability |
| Connecting tools to agents | Model Context Protocol (MCP) servers | Build integrations once, reuse across agents |
Need help choosing the right agent stack?
Neonicz builds model-agnostic agents and runs them as managed Agents-as-a-Service.