From Chatbot to Automations

The evolution of AI workflows

In our previous lessons, we explored how structured prompts can be woven into conversational interfaces through chatbots.

Now, let's take the next step in our PromptOps journey: creating AI agents and automated workflows that operate more independently within your content systems.

While chatbots are powerful tools, they still require your active involvement. You must open the interface, type your query, and wait for a response.

But what if your AI tools could operate more autonomously—taking action at specific times or in response to specific events?

Imagine you're a technical writer maintaining documentation for software that updates monthly. Currently, you manually update your documentation when new features are released.

What if instead, your system could automatically detect new release notes, analyze them, and draft updated documentation without your intervention?

Or perhaps you're an educator who's created a chatbot that provides feedback on student writing.

Rather than requiring students to copy and paste their work into a chat interface, what if they could simply submit their assignments to a shared folder, triggering an AI system to provide initial feedback before you review it?

This shift from reactive to proactive AI implementation represents a significant advancement in how we collaborate with machines, but raises an important question:

What if we could take the structured prompting techniques we've developed and apply them in systems that don't wait for our commands, but instead recognize when action is needed and respond accordingly?

The Promise of AI Agents

This is where AI agents and automated workflows enter the picture. But let's be clear about what these terms actually mean, as they're often surrounded by unnecessary mystique:

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AI Agent: At its simplest, an AI agent is just an AI system that can perform specific tasks with some degree of independence. It's not a sentient being or a complex autonomous system – it's essentially a chatbot with a job description and the ability to act when certain conditions are met rather than waiting for direct commands.

Automated Workflow: This is a series of connected steps where an AI agent works together with other digital tools to accomplish tasks with minimal human intervention. These workflows follow an "if this, then that" logic – when specific triggers occur, the workflow activates, performs its tasks, and delivers results.

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Together, these concepts represent a natural progression in machine rhetorics:

1. We started with structured prompts that generate single responses.

2. We evolved to chatbots that maintain context across multiple exchanges.

3. Now we're developing agents that operate proactively within defined workflows.

The transition from chatbots to automation isn't about abandoning our conversation-based tools but extending them beyond the chat interface into our broader content operations. It's about taking the same principles of structured prompting that make chatbots effective and applying them in systems that can operate even when we're not actively engaged with them.

For content professionals, educators, and technical communicators, this evolution offers tremendous potential to streamline repetitive tasks while preserving your expertise for the work that truly requires human insight and creativity.

Consider these common scenarios that might resonate with your daily work:

You're a technical writer frustrated with how a summarization tool misses key points that matter to your product's users. The tool consistently overlooks details about accessibility features and security considerations that your readers specifically care about.

As a content creator, you're tired of a style checker that enforces rules that don't align with your publication's voice. It flags your intentional sentence fragments as errors and pushes for formal language when your audience responds better to a conversational tone.

Instead of abandoning these tools or accepting their limitations, you can rebuild them—creating versions that better align with your specific needs and preferences.

A technical writer might create a workflow that first runs content through a general summarization tool, then applies a custom filter that ensures accessibility and security information is preserved or even emphasized.

A content creator could build a system that applies house style rules after the general check, effectively overriding generic suggestions with publication-specific guidance.

This creative approach echoes a long tradition in writing and publishing:

Dissatisfied with the books being published? Write your own. Unhappy with information available on the web? Create your own content. Concerned about biased AI systems? Build alternatives that reflect your values.

The ability to design AI workflows gives you tremendous agency in shaping how technology serves your writing and content processes.

Cunning Intelligence in Action

The ancient Greeks had a concept called metis—often translated as "cunning intelligence" or "adaptive wisdom." This was the kind of practical intelligence embodied by figures like Odysseus, who used creativity and resourcefulness to overcome challenges.

Unlike theoretical knowledge (episteme) or technical skill (techne), metis represented the ability to adapt, improvise, and find clever solutions to complex problems.

When you design AI workflows, you're exercising a modern form of metis. You're not just accepting the tools and processes given to you; you're actively reshaping them to better serve your needs and goals. You're finding creative ways to make AI systems work for your specific context, even when they weren't explicitly designed for your particular use case.

Consider this real-world example from a technical writing team at a large software company.

The team was responsible for documenting an increasingly complex product that received updates every two weeks. Their challenge wasn't just keeping up with the volume of changes but ensuring consistency across hundreds of interconnected help articles. Standard documentation tools didn't offer effective ways to track these relationships or identify which articles needed updating when features changed.

Rather than accepting these limitations, the team created a custom workflow that:

1. Monitored the development team's code repository for changes to specific feature areas

2. Automatically extracted key terms and functions from the updated code

3. Searched their documentation for all articles mentioning these terms

4. Generated a prioritized list of potentially affected documents with confidence scores

5. Created first drafts of change notes for the highest-confidence matches

This system didn't just save time—it transformed how the team approached documentation. Instead of frantically reviewing all documents after each release, they could focus their attention precisely where it was needed most. The system wasn't perfect, occasionally missing connections or suggesting unnecessary updates, but it dramatically improved their process.

What made this approach a true expression of metis wasn't just the technology but how it embodied the team's unique expertise and knowledge of their documentation ecosystem.

They built a system that reflected their understanding of how their particular product's features connected to their specific documentation structure—knowledge that no general-purpose AI tool could possibly possess.

This represents a significant shift in our relationship with AI:

• Instead of being constrained by the limitations of existing tools, you create new ones tailored to your specific needs.

• Rather than adapting to how machines work, you adapt the machines to how you and your team work.

• Instead of accepting biased or problematic AI outputs, you design systems that align with your values and priorities.

By building AI agents and workflows, you become an architect of automated processes rather than merely a user of them. You're essentially writing the instructions not just for what content should be created, but for how your entire content system should operate.

The technical writing team didn't just create a tool—they extended their collective intelligence and expertise into an automated system that embodied their best practices and deepest knowledge of their product.

The system didn't replace their expertise; it amplified it, allowing them to apply their judgment and insight more effectively by eliminating hours of tedious document review.

This form of metis—the ability to creatively reshape technology to serve human needs—represents one of the most powerful aspects of machine rhetorics. It's not about mastering AI for its own sake, but about bending these technologies to better serve your specific content operations challenges.

The Anatomy of an AI Workflow

Today's marketplace is flooded with AI automation tools with impressive-sounding capabilities. From "intelligent content optimization platforms" to "autonomous marketing assistants" to "self-improving documentation systems," these tools often present themselves as revolutionary technologies with proprietary approaches.

But here's a secret that will help you navigate this landscape and eventually build your own systems: beneath their sleek interfaces and marketing language, virtually all AI automation tools share the same fundamental architecture. They all require the same four core components to function, regardless of their specific application or industry focus.

Let's examine these universal building blocks while looking at some popular tools in the market:

1. Trigger

This is the event or condition that initiates the workflow. A trigger might be:

• A specific time (every morning at 8 AM)

• A new piece of data (a fresh article published on a website)

• A user action (clicking a browser extension button)

• A system event (a new file being added to a folder)

Real-world examples:

• Zapier's automation platform calls these "Triggers" explicitly in their interface

• HubSpot's marketing automation calls them "Enrollment criteria"

• Microsoft Power Automate refers to them as "When this happens..."

• IFTTT (If This Then That) builds the trigger concept directly into its name

For technical writers, a valuable trigger might be when documentation files are modified in your content management system, prompting an automated quality check. Content creators might use the publication of competing content as a trigger to generate differentiation analyses. Educators could set up triggers based on assignment submission deadlines to automatically distribute work for peer review.

2. AI Agent

This is the artificial intelligence component that performs the core task. The agent leverages structured prompting techniques but operates without direct human supervision.

Real-world examples:

• Copy.ai calls this their "AI writer"

• Jasper refers to its "AI assistant"

• GitHub Copilot calls it their "AI pair programmer"

• Grammarly's system functions as an "AI writing assistant"

Despite the different names, these are all essentially AI systems following structured instructions to process information and generate outputs. The quality and usefulness of these systems depend largely on how well their prompts are structured—exactly what we've been exploring throughout this course.

For example, a technical documentation team might employ an agent that reviews newly written procedures and suggests clarifications based on readability scores and common user questions. Content marketers might use an agent that transforms long-form content into multiple social media posts with appropriate hashtags. Educational content developers could implement an agent that converts lecture notes into interactive quiz questions to reinforce learning.

3. Connections

These are the integration points between your AI agent and other systems. Connections might include APIs to access external data, file system interfaces, email services, or database connections.

Real-world examples:

• Make.com (formerly Integromat) calls these "Modules"

• Airtable's Automations refers to them as "Actions"

• Notion AI integrates with your existing Notion database

• Salesforce's Einstein connects with your customer data

These connections are what make AI agents truly powerful. A technical writing workflow might connect to your product roadmap system to automatically flag documentation that needs updating based on upcoming feature changes. Content creators might establish connections to analytics platforms to inform content optimization decisions. Educators could connect their grading systems with content repositories to automatically suggest resources based on student performance patterns.

4. Output

This is the result produced by the workflow. The output might be a document stored in a specific location, an email sent to stakeholders, data added to a spreadsheet, or even a trigger for another workflow.

Real-world examples:

• Buffer delivers social media posts to multiple platforms

• Canva's Magic Write generates design copy

• Fathom AI produces meeting summaries

• Notion AI creates formatted content in your workspace

For technical communicators, an output might be a weekly report highlighting potential inconsistencies across your documentation suite. Content creators might receive a personalized digest of trending topics in their niche with AI-generated content ideas tailored to their publication's style. Educational content developers might produce automatically differentiated versions of the same learning materials targeted at various student proficiency levels.

Understanding the Patterns Behind the Products

By recognizing these common patterns across different AI automation tools, you gain several advantages:

1. Better evaluation skills: You can more effectively assess whether a new tool actually offers unique capabilities or just repackages familiar components.

2. Improved integration abilities: Understanding these shared structures helps you connect different tools into more powerful custom workflows.

3. Greater independence: When you understand the basic architecture, you can build your own solutions when commercial tools don't quite meet your needs.

4. Strategic perspective: Rather than getting caught up in feature comparisons, you can focus on how well a tool's architecture aligns with your specific content operations.

This perspective also reveals an important truth: the most sophisticated AI automation isn't necessarily the one with the most advanced AI model, but the one that most effectively orchestrates these four components to address your specific content challenges.

As we continue our exploration of automation, remember that you're not just learning to use tools—you're learning to think about content operations in a way that allows you to design your own tools when necessary, combining these universal building blocks in unique ways that address your specific needs.

Starting Simple: Your First AI Workflow

While the potential of AI workflows is vast, complexity is the enemy of reliability. Just as we developed chatbots iteratively, we'll approach workflow automation with a similar mindset:

• Start with a single, well-defined task

• Make sure it works consistently before adding complexity

• Add components incrementally, testing at each stage

• Focus on creating value, not technical sophistication

For technical writers, a simple first automation might monitor your product's GitHub repository for documentation-related issues and compile them into a daily digest. This targeted workflow accomplishes one specific task reliably rather than attempting to automate your entire documentation process at once.

Content creators might begin with a workflow that identifies trending hashtags in your niche and suggests content ideas based on them. This focused automation addresses a specific pain point—ideation—before expanding to more complex aspects of your content creation process.

Even these simple automations can dramatically improve your productivity and effectiveness. A workflow that checks RSS feeds for new content in your field, summarizes the articles, and emails you the results each morning might take just a few hours to build but save you hours of work each week.

The Writer as System Designer

Throughout this course, we've emphasized that working with AI is fundamentally an act of writing—crafting language to achieve specific outcomes. Building automated workflows extends this perspective, positioning you as both a writer and a system designer.

This evolution is particularly meaningful for professionals who work with content.

Technical writers have always served as bridges between complex systems and their users. Now, you're not just documenting systems—you're designing them. The same skills that help you create clear documentation—understanding user needs, breaking complex processes into manageable steps, ensuring consistency—are valuable assets in designing effective AI workflows.

Content creators already think systematically about audience journeys and engagement funnels. Designing AI workflows leverages this strategic thinking in new ways, allowing you to automate parts of the content lifecycle while maintaining your distinctive voice and perspective.

When you build an AI workflow, you're still writing—not just prompts for the AI, but instructions for how various components should interact. You're creating a system that processes language, generates language, and delivers language to specific destinations. You're essentially writing a meta-narrative that guides how content flows through your professional ecosystem.

Case Study: The Content Monitor

Let's explore how a chatbot might evolve into an automated workflow.