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Zarif Automates

Agents & AI Engineering

Building, evaluating, and running AI agents: frameworks, development environments, memory, tools, MCP, Codex and Claude Code in practice.

Choose your agent environment

Start with the three meanings of agent development environment: a coding product, an RL task system, or an execution runtime. Then inspect the RL environment projects or keep the short definition handy.

Start with

A focused reading order to help you choose your next step.

  1. Agent Development Environments: Coding Products, RL Tasks and Runtimes

    Separate the three meanings of agent development environment: coding-agent products, RL training environments and hosted execution runtimes.

  2. Your Research Agent Needs an Evidence Ledger Before It Needs a Better Prompt

    A practical research-agent design: preserve sources, separate facts from inferences, and deliver a decision someone can review.

  3. What Is Model Context Protocol (MCP)? The Complete 2026 Guide

    Learn what Model Context Protocol (MCP) is, how it works, and why it's the universal standard connecting AI agents to tools and data.

  4. How to Build a Multi-Agent AI System from Scratch

    Step-by-step tutorial for building a multi-agent AI system from scratch using CrewAI, LangGraph, or AutoGen — with architecture patterns and production tips.

  5. How to Deploy AI Agents to Production

    A 7-step engineer's guide to deploy AI agents production-ready in 2026: hosting, state, observability, evals, retries, cost controls, and rollouts.

  6. How to Monitor and Debug AI Agents

    Learn how to monitor and debug AI agents with traces, metrics, alerts, and replay evals. Stop guessing why your agent failed in production.

All posts in Agents & AI Engineering(67)

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