AI Tools & Ecosystem
Understanding the landscape of AI tools and how they work — from agents to wrappers to the tools worth knowing.
INFO
- Time: ~20 minutes
- Difficulty: Beginner
- What you'll learn: How AI tools fit together and which ones are worth your time
This Page Covers
- What Are Agents? - AI that can take actions autonomously
- Wrappers vs Base Tools - The difference between using AI directly and apps built on top
- How Tool Calling Works - What happens when AI "uses" external tools
- Interesting Tools - A curated list of AI tools worth exploring
What Are Agents?
You'll hear "AI agents" mentioned everywhere. Here's what it actually means:
An AI agent is AI that can take actions on its own to accomplish a goal - not just answer questions, but actually do things.
Regular AI vs Agents
| Regular AI | AI Agent |
|---|---|
| You ask a question, it answers | You give a goal, it figures out steps |
| One response, done | Multiple actions in sequence |
| Can only generate text | Can interact with other tools and systems |
| You control each step | It decides what to do next |
Examples of AI Agents
ChatGPT Plugins/Actions: When you ask ChatGPT to "book a flight" and it searches Expedia, compares options, and shows you results - that's agent behavior. It's taking multiple actions to achieve your goal.
Claude Computer Use: Claude can control a computer - clicking, typing, navigating - to accomplish tasks. You could say "fill out this form with my information" and it actually operates the browser.
Coding Assistants: Tools like Cursor or GitHub Copilot Workspace can read your codebase, make changes across multiple files, run tests, and iterate - all from a single request.
Current Limitations
Agents sound amazing, but they have real limitations:
- They make mistakes - And mistakes compound. One wrong step early can derail everything
- They're slow - Taking multiple actions takes time
- They're expensive - Each action costs money (API calls add up)
- They need guardrails - Without limits, agents can take unexpected actions
- They struggle with ambiguity - Clear goals work better than vague ones
Realistic Expectations
Think of current AI agents as capable interns. They can handle well-defined tasks with clear success criteria, but they need supervision and shouldn't be trusted with anything irreversible without review.
Wrappers vs Base Tools
When you use AI, you're usually using one of two things:
Base Tools (Direct from the Source)
These are AI interfaces provided directly by the companies that build the models:
| Tool | Company | Model |
|---|---|---|
| ChatGPT | OpenAI | GPT-5.2 (Instant, Thinking, Pro) |
| Claude | Anthropic | Claude Sonnet, Opus, Haiku |
| Gemini | Gemini 3 |
Advantages:
- Latest features first
- Full model capabilities
- Direct relationship with provider
- Most reliable uptime
Disadvantages:
- Generic interface (not specialized for your use case)
- You're limited to what they build
Wrappers (Built on Top)
These are apps that use AI models via API to create specialized experiences:
Examples:
- Jasper - Marketing copy tool (uses GPT under the hood)
- Copy.ai - Content generation (uses multiple models)
- Notion AI - Writing assistant within Notion
- GitHub Copilot - Code completion (uses OpenAI models)
Advantages:
- Specialized for specific workflows
- May have better UX for certain tasks
- Often combine multiple AI capabilities
Disadvantages:
- May be more expensive (paying for wrapper + underlying API)
- Dependent on another company's API access
- Features may lag behind base tools
- Limited by what the wrapper chooses to expose
How to Evaluate Wrappers
Before using a wrapper tool, ask:
- What model does it use? - Can you do this in the base tool?
- What does it add? - Is there real value beyond the base model?
- What does it cost? - Compare to just using the base tool
- What's the business model? - Will it exist in 6 months?
- What do you give up? - Data privacy, flexibility, latest features?
Wrapper Risk
Many AI wrappers are thin layers over base models with little differentiation. When base tools add features, wrappers can become obsolete quickly. Invest time in learning base tools first.
How Tool Calling Works
When ChatGPT "searches the web" or "runs Python code," it's using a capability called tool calling (or function calling). Understanding this demystifies what's happening.
The Basic Process
You ask something that might need a tool
- "What's the weather in Paris?"
AI recognizes it needs a tool and outputs a structured request
- AI doesn't search itself - it outputs:
{"tool": "weather", "location": "Paris"}
- AI doesn't search itself - it outputs:
The system executes the tool and returns results
- The weather API is called, returns temperature/conditions
AI receives the result and incorporates it into its response
- "The weather in Paris is currently 18°C and partly cloudy"
Why This Matters
Understanding tool calling helps you:
- Know what's actually happening - AI isn't magic; it's orchestrating external tools
- Understand limitations - Tools can fail, return errors, or have outdated info
- Debug when things go wrong - If search gives bad results, it's the search tool, not the AI's "knowledge"
- Predict capabilities - AI can only use tools it has access to
Common Tools AI Can Use
| Tool Type | What It Does | Examples |
|---|---|---|
| Web Search | Finds current information | Bing, Google |
| Code Execution | Runs code and returns output | Python interpreter |
| Image Generation | Creates images from descriptions | DALL-E |
| File Reading | Processes uploaded documents | PDF parser |
| Calculator | Performs precise math | Calculator API |
Tools Don't Equal Intelligence
Just because AI can use a calculator doesn't mean it's "good at math." It means:
- AI recognized this needs calculation
- AI formatted the request correctly
- A calculator did the actual math
- AI presented the result
This is powerful, but it's orchestration, not reasoning.
Interesting Tools Worth Exploring
Here are some AI tools that solve specific problems well:
Image Generation
Gemini with Imagen 3 (NanoBanana)
Google's Gemini now includes image generation. It's particularly good at:
- Photorealistic images
- Text in images (logos, signs)
- Specific styles when prompted well
How to use: Ask Gemini to create an image directly in conversation.
Presentation Creation
Gamma App (gamma.app)
Creates presentation slides from descriptions or documents.
Good for:
- Quick pitch decks
- Internal presentations
- Converting documents to slides
- First drafts to refine
Limitations:
- Generic templates
- May need significant editing for important presentations
- Better for speed than polish
Experimenting with AI
Google AI Studio (aistudio.google.com)
Free access to Google's latest AI models, including beta versions.
Good for:
- Trying new models before they're widely available
- Comparing model responses
- Building simple AI applications
- Learning how prompts affect different models
Features:
- Access to Gemini models (including experimental versions)
- Prompt tuning tools
- Simple app creation
- API access for developers
Free Experimentation
Google AI Studio is free to use with generous limits. It's a great way to experiment with prompts and compare how different models respond to the same input.
Key Takeaways
- Agents are AI that take actions - Powerful but still limited; treat them like capable interns
- Wrappers build on base tools - Evaluate what value they add before committing
- Tool calling is orchestration - AI formats requests, external tools do the work
- Base tools first - Learn ChatGPT/Claude directly before specialized wrappers
- New tools constantly emerge - The landscape changes fast; stay curious but selective