AI Engineer Portal
Your personal operating system for career transition.
AI Agents and Tools
Understand when agentic patterns help and how to keep them safe.
Tool design for LLM agents
Design function schemas, input validation, return contracts, and error envelopes that LLMs can call reliably.
Tool design for LLM agents
Design function schemas, input validation, return contracts, and error envelopes that LLMs can call reliably.
Planning and reasoning loops
Implement ReAct, plan-then-execute, and reflection patterns so agents can decompose and solve multi-step tasks.
Multi-agent orchestration
Coordinate multiple specialized agents using supervisor patterns, handoffs, and structured message passing.
Agent memory and state
Manage conversation history, sliding window memory, summarization, and state persistence to keep agents effective across long interactions.
Benchmarking and guardrails
Build evaluation harnesses to score agent task completion and implement safety guardrails for production agent systems.
LangGraph for agent orchestration
Model agents as graphs with StateGraph, route between nodes conditionally, and add checkpointing for human-in-the-loop workflows.
Agent framework comparison
Evaluate LangGraph, CrewAI, Anthropic tool_use, and AutoGen across real criteria and build a decision matrix for choosing a framework.
Model Context Protocol (MCP) for tool integration
The emerging standard for connecting AI models to external tools and data sources.
MCP (Model Context Protocol)
The emerging standard for connecting LLMs to external tools and data: architecture, building servers and clients, and when to use MCP versus rolling your own tool integration.