Section 3: Practical examples
Module 8 — Agentic AI: When AI Starts Taking Actions
Section 3: Practical Examples
Purpose of This Section
This section illustrates what agentic AI looks like in real-world workflows and how it differs from traditional, single-response AI usage.
The goal is to make agentic AI recognizable, not abstract.
The Core Idea
Agentic AI operates through task chains, not isolated actions.
Instead of producing one answer, agentic AI carries out a sequence of steps tied together by a goal. These steps may span multiple tools, documents, or systems and are executed with minimal human intervention between stages.
The value comes from reducing friction, not replacing judgment.
What Agentic Workflows Look Like
Agentic AI is most effective when tasks are:
repetitive
structured
predictable
rule-based
These workflows often already exist informally, but are manually coordinated by humans.
Common Practical Examples
Inbox to Action
Agentic AI can review inboxes, identify priority messages, summarize content, draft replies, and queue actions for human approval.
PDF to Finished Document
Agentic AI can extract key data from documents, organize information, populate templates, and prepare structured outputs for review.
Report to Presentation
Agentic AI can identify themes, create slide outlines, draft speaker notes, and format presentation materials.
Notes to Calendar
Agentic AI can convert meeting notes into tasks, assign owners, and propose calendar entries.
Each example represents a connected workflow rather than a single response.
Where Agentic AI Works Best
Agentic AI performs well when:
goals are clearly defined
steps are repeatable
success criteria are explicit
outputs can be reviewed
It excels at execution within boundaries.
Where Caution Is Required
Agentic AI struggles when tasks involve:
political or social nuance
ambiguous decision-making
legal or compliance judgment
high reputational or financial risk
These areas require deliberate human involvement.
Common Failure Mode
A common mistake is focusing on dramatic or flashy automation instead of practical use cases.
High-impact value often comes from automating small, repetitive tasks that drain attention rather than from replacing complex decision-making.
Another failure mode is allowing agents to operate without review or approval.
The Conjugo Rule
Automate the boring. Supervise the important.
Agentic AI should remove friction, not accountability.
Best Practices
Practical agentic workflows work best when:
humans approve meaningful actions
boundaries are clearly defined
automation is incremental
oversight is continuous
Effective agentic systems are constrained by design.
Section Takeaway
Agentic AI connects steps into workflows
Value comes from reducing repetitive work
Human judgment remains central
Oversight determines success
Agentic AI is most powerful when it quietly handles what humans should not have to.
This concludes Section 3.