01
LLM Foundations & Prompt Engineering
Structuring prompts, system messages, and context windows so a model behaves reliably, not just once. Few-shot examples, reasoning scaffolds, and output formatting that downstream code can actually parse.
02
Agentic Systems & Tool Use
Giving a model tools it can call (calendars, CRMs, search, code execution) and a loop that lets it plan, act, observe, and decide what to do next: the core pattern behind MateOS and Kyrios.
03
Multi-Agent Orchestration
Coordinating several specialized agents that hand off work, share context, and report completion back to a shared protocol, the pattern Workmancer's AI Employees run on.
04
RAG & Knowledge Retrieval
Chunking, embedding, and retrieving the right context from a knowledge base so an agent answers from real data instead of guessing, with hybrid search when keyword matching still matters.
05
Model & API Integration
Working directly with the Anthropic Claude API and OpenAI API: streaming responses, function calling, rate limits, and keeping token costs predictable once a system is in production.
06
Workflow Automation
n8n, Make, and Zapier for the plumbing every agent needs around it: triggers, webhooks, scheduled jobs, and connecting a dozen SaaS tools without writing a backend from scratch.
07
Evaluation, Guardrails & Fallbacks
Testing what an agent actually does against what it's supposed to do, then building the explicit fallback and handover logic for the cases it shouldn't be trusted to decide alone.
08
Data Pipelines & Python
Python for the glue code: web scraping, REST API integration, data cleaning, and the scripts that keep an agent's inputs accurate and its outputs structured.