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Technical Basics

AI Workflow Engineering

This course covers chaining AI calls into reliable, state-driven workflows and multi-agent pipelines. You will learn to construct function schemas for tool-calling, manage memory handoffs across sub-agents, and configure retry behaviors. By the end, you will deploy resilient agent networks that handle API exceptions gracefully.

Created by SkillCite Instructor
Last updated Aug 2026
AI Workflow Engineering
Free
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This course includes:
  • 4 weeks of structured content
  • 5 video lessons & readings
  • 4 hands-on lab exercises & quizzes
  • Verified certificate on completion
What you'll learn

Learning Outcomes

By the end of this course, you'll be able to:

Deconstruct single-prompt tasks into state-driven multi-step workflows.
Configure LLM tool-calling actions to execute database queries.
Manage information handoffs and memory logs across sub-agents.
Design graceful exception-handling steps to manage API failures.
Build production-ready agent networks using state machine logic.

How this course opens doors

State Orchestration

Design state machines to coordinate multi-step workflows and sub-agent handoffs.

Tool-Calling

Configure models to select and execute API tools based on user inputs.

Error Resilience

Implement retry parameters and fallbacks to handle model failures.

4w
Access Duration
Course Content

Course curriculum

4 modules · 17 lessons
01
1. From Single Calls to Workflows
~31 min
Why Single Prompts Aren't Enough~5 min
Anatomy of an AI Workflow~6 min
Single-Shot vs Multi-Step AI Tasks~5 min
Common Workflow Shapes~5 min
Module 1 Quiz — Foundations of Workflows~10 min
02
2. Agents & Tool-Calling
~25 min
03
3. State, Memory & Multi-Step Coordination
~25 min
04
4. Reliability, Failure Handling & Production Workflows
~25 min
Skills you'll gain

Skills Cloud

State Machine LogicLLM Tool SelectionSub-Agent CoordinationGraceful RetriesMemory Sync MethodsProduction Orchestration
Target Audience

Who this course is for

Backend Developers

Build multi-step agent pipelines, configure tools, and manage state.

Solutions Architects

Design scalable agent topologies and manage memory synchronizations.

Data Engineers

Orchestrate automated data analysis loops using API calling blocks.

Offered by

Your instructor

SI

SkillCite Instructor

SkillCite AI

86 courses

SkillCite Industry Expert Instructors bring decades of combined real-world expertise directly to you, making complex concepts simple.

Certification

Earn a Verifiable Certificate

Certificate of Completion
Learner Name
AI Workflow Engineering
SC-2026-VERIFIED
Verified

SkillCite Verified Certificate

Complete all modules and pass the final assessment to earn a shareable, verifiable digital credential.

Verifiable via public API with unique credential ID
Shareable on LinkedIn, Twitter, and personal portfolio
Recognized by employers and technology companies
Week-by-Week

Course Timeline

Week 1
Workflow State Topologies

Map single prompts to state machines, write control structures, and save memory variables.

State machinesControl flowsMemory slots
Week 2
Tool-Calling Configurations

Define JSON function schemas, handle execution outputs, and manage validations.

Function schemasExecution loopsValidation checks
Week 3
Multi-Agent Coordination

Build supervisor agents, map sub-agent roles, and pass context parameters.

Supervisor botsSub-agent routingContext handoffs
Week 4
Error Resilience & Safety

Configure retry behaviors, monitor infinite loops, and deploy production pipelines.

Retry behaviorsLoop limitsSafety guardrails
Common Questions

FAQ

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