AI agent

Why We Built PlugPlay: The Painful Truth About Building AI Agents

May 13, 2025

Ever tried to build the same IKEA furniture five times in a row? That's what developing AI agents felt like before we built PlugPlay.AI.

Let me take you behind the scenes of our most frustrating technical challenge at CreativeScript—and how we turned that pain into something that might just save you months of development time.

The Groundhog Day of AI Development

Three months ago, our team was drowning.

As a design + dev studio helping founders ship AI products, we found ourselves in a bizarre loop: rebuilding the same infrastructure for every single client pxroject.

Monday: Set up OpenAI integration for a customer support bot. Tuesday: Configure conversation memory storage in MongoDB. Wednesday: Build real-time response streaming with WebSockets. Thursday: Add error handling for rate limits. Friday: Repeat for a completely different client.

Each time, we'd write nearly identical code with minor variations. The worst part? We knew we were accumulating technical debt faster than we could manage it.

"This is the fourth time I've written this exact WebSocket streaming code," our lead developer complained during a particularly late night. "There has to be a better way."

Where Everything Breaks

The breaking point came during a critical client demo.

We had built what seemed like a solid agent—until it hit OpenAI's rate limits mid-presentation. No fallback, no graceful recovery. Just an error message and awkward silence.

Later that week, another agent started forgetting conversation context because we hadn't properly tuned the memory window. And don't get me started on the time a client wanted to switch from OpenAI to Anthropic, requiring us to rewrite chunks of perfectly functional code.

Each failure exposed the same underlying problem: we were treating every AI agent as a custom, one-off solution rather than recognizing the common patterns.

The Turning Point: Modularity or Bust

During a frustrated team retrospective, someone asked a simple question: "What if we built this once, really well, and never had to rebuild it again?"

It was embarrassingly obvious. We needed a modular framework that would:

  • Abstract away LLM provider differences

  • Offer multiple memory strategies without reinventing them

  • Handle streaming, errors, and rate limits elegantly

  • Allow for tool integration without spaghetti code

We decided to invest two weeks of engineering time to build what would become PlugPlay.AI. The name reflected our goal: to make building AI agents as simple as plugging in the components you need and playing with the result.

Under the Hood: What We Actually Built

PlugPlay.AI isn't rocket science—it's just good engineering applied to a repetitive problem. Here's what we created:

1. The LLM Adapter Layer

# Before PlugPlay: Provider-specific code everywhere
if provider == "openai":
    response = await openai.ChatCompletion.create(...)
elif provider == "anthropic":
    response = await anthropic.completions.create(...)
# And so on...

# With PlugPlay: One clean interface
llm = LLMProviderFactory.create_llm(provider, model)
response = await llm.generate(prompt)

This adapter pattern lets you switch between OpenAI, Anthropic, or any future provider by changing a single configuration parameter.

2. Memory Management That Actually Works

We implemented four memory types:

  • Buffer Memory: Stores all messages (simple but can get large)

  • Window Memory: Keeps a fixed number of recent messages

  • Summary Memory: Uses LLM to summarize conversation history

  • Summary+Buffer: Combines summary with recent messages

Each has its use case, and switching between them is a single parameter change:

bot_config = {
    "llm_provider": "anthropic",
    "llm_model": "claude-2",
    "memory_type": "summary",  # Just change this line
    "system_prompt": "You are a helpful assistant."
}

3. Real-Time Streaming That Doesn't Suck

WebSockets are notoriously finicky. We standardized the connection handling, authentication, and streaming process:

# Server-side streaming
async def chat(bot_id, user_id, message):
    async for chunk in bot_service.stream_chat(bot_id, user_id, message):
        yield chunk

The result? Typing indicators and token-by-token responses that feel responsive, not janky.

4. Tool Integration That Scales

We built a registry for tools that agents can use:

@tool
def search_wikipedia(query: str) -> str:
    """Search Wikipedia for information about a query."""
    # Implementation...

register_tool("wikipedia", create_wikipedia_tool)

Add as many tools as you want—web searches, API calls, database queries—without touching the core agent logic.

5. The Error Handling We Wish We Had Earlier

Rate limits? Handled. Provider outages? Fallback providers kick in. Long-running operations? Background processing with status updates.

All the things we painfully learned through production failures, baked into the framework.

Why This Matters for Founders

If you're building AI products, you're probably facing the same challenges:

  1. Speed to market is everything. PlugPlay cuts development time from weeks to hours.

  2. Technical debt kills startups. A solid foundation means you can iterate on what matters—the unique value of your AI product.

  3. AI infrastructure is a distraction. Your competitive advantage isn't in how you connect to OpenAI; it's in what your product actually does.

  4. Flexibility preserves optionality. When the next breakthrough LLM drops, you should be able to adopt it immediately, not after a sprint of refactoring.

We've used PlugPlay to ship seven production agents in the last month alone—each with completely different purposes but built on the same solid foundation.

What's Next for PlugPlay

We're building PlugPlay in public because we believe in open infrastructure. Over the next few weeks, we'll be sharing:

  • Detailed architecture decisions and tradeoffs

  • Performance benchmarks across different memory types

  • How we handle authentication and monitoring

  • Tool integration patterns for common use cases


At CreativeScript, We're Building the Future

We specialize in designing AI-powered systems where agents, tools, and workflows seamlessly integrate.
Our mission is to help founders validate ideas quickly and scale efficiently by focusing on outcomes, not just outputs.

Takeaway: Embrace AI orchestration to transform your business processes and achieve measurable results.

Ready to move beyond basic AI applications?
Let's explore how orchestrated AI agents can drive real outcomes for your business.
Book your free AI consultation session.

This is the first post in our "Building PlugPlay in Public" series. At CreativeScript, we help startup founders ship faster using AI. Learn more about our approach.