Agentic AI Workflows
Master the patterns, architectures, and best practices for building reliable AI agent systems — from simple chains to fully autonomous workflows.
Agentic AI Workflows
Build AI systems that go beyond single prompts. This path teaches you the proven patterns for chaining, routing, parallelizing, and orchestrating LLM calls — then shows you how to put them into production.
Each module builds on the last, taking you from the foundations of agentic AI to deploying reliable, cost-effective agents in real-world applications.
Modules
1 — Agentic AI Fundamentals
What makes an AI system agentic, the augmented LLM, and when to use agents vs. workflows.
2 — Workflow Patterns
Prompt chaining, routing, and parallelization — the building blocks of every AI workflow.
3 — Advanced Orchestration Patterns
Orchestrator-workers, evaluator-optimizer loops, and autonomous agent architectures.
4 — Tool Use & Function Calling
Design effective tools, implement function calling, and connect agents to external APIs.
5 — Planning, Memory & Evaluation
Task decomposition, context management, and systematic evaluation of agent outputs.
6 — Building Production Agents
Reliability, cost optimization, testing strategies, and real-world deployment patterns.
Who this path is for
This path is designed for developers who have worked with LLM APIs and want to move beyond single-call prompts into multi-step, reliable AI systems. You'll learn architecture patterns drawn from industry research (Anthropic, academic literature) and see how they apply to real tools like Skytells Orchestrator and the Skytells AI API.
What you'll build understanding of
By the end of this path, you'll have a mental framework for:
- Choosing the right architecture for any AI task
- Designing tool interfaces that LLMs can use reliably
- Building evaluation pipelines that catch failures before users do
- Deploying agents that are cost-effective and observable