Production Ai Agents Reliable Business Workflows

Production Ai Agents: Reliable Business Workflows
Published 8/2026
Created by Dr. Amar Massoud
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 58 Lectures ( 5h 35m ) | Size: 4.5 GB
Design, evaluate, monitor & govern agent workflows that survive real business complexity.
What you'll learn
Decide when NOT to use an agent and when a simple automation wins
Distinguish single-agent, sequential-workflow, router, multi-agent, and human-in-the-loop designs
Design agent roles, handoffs, schemas, and shared memory that prevent context loss
Build evaluation rubrics, golden datasets, and regression tests for agent outputs
Add observability with traces, tool calls, and cost to catch loops, drift, and failures
Create governance controls, approval gates, and audit logs for business-critical actions
Package an agent workflow for a safe pilot launch with a rollback plan
Requirements
Basic understanding of LLMs and prompts
Optional: light Python familiarity for the implementation call-outs
Business-process thinking helps; no framework lock-in required
Description
This course contains the use of artificial intelligence.
Most agent courses teach you to build. This one teaches you to be trusted.
The market is full of courses that stack up 100 labs, 100 agents in 100 days, or five frameworks in a weekend. They are fun, and the reviews say the same thing over and over: not production depth. They show you how to make an agent do something clever once, but never how to make it reliable enough to put in front of a real business, with real money and real customers on the line.
This course lives in that gap. Using one running case study, Cascade Commerce, a mid-market retailer whose splashy multi-agent demo collapsed in production, you will learn the production judgment that decides whether an agent workflow ships or embarrasses you.
Architecture decisions: single agent versus sequential workflow versus router versus multi-agent versus human oversight, and when not to use an agent at all.
Business-process modeling: start from the job-to-be-done, and map inputs, outputs, decisions, and the exceptions where demos die.
Context and handoffs: typed schemas and shared state that stop context leaking between agents.
Evaluation: golden datasets, rubrics, failure taxonomies, and regression tests for the day you swap the model.
Observability: traces, tool calls, and cost, built on OpenTelemetry ideas, to catch loops and drift before your users do.
Governance: tool permissions, approval gates, and audit logs mapped to NIST AI RMF and the OWASP agentic risks.
Pilot launch: scope, success metrics, rollback, and change management.
This whole approach stays provider-neutral, so the architecture does not age with any single framework, and it ships templates you will reuse for years, including a discovery worksheet, an architecture decision matrix, role cards, handoff schemas, an evaluation rubric, an observability checklist, a governance matrix, and a pilot launch checklist. You will move from making an agent demo, to designing a production-ready agent workflow with clear responsibilities, context, controls, and evaluation.
Who this course is for
AI builders and developers who can make an agent demo but not a reliable one
Automation consultants, technical PMs, and operations managers
Business analysts who want production-grade agent workflows, not toy demos
Homepage

You do not have permission to view the full content of this post. Log in or register now.

You do not have permission to view the full content of this post. Log in or register now.