The Framework Trap
I've spent the last year watching teams struggle with AI agent implementations. The pattern is always the same: they start by choosing a complex framework, spend months wrestling with abstractions, and end up with something that's harder to maintain than it needs to be. Meanwhile, the teams having the most success are building with surprisingly simple patterns.
Here's the thing about AI agents: they don't need fancy frameworks to be effective. In fact, those frameworks often get in the way. Let me show you what actually works.
When Agents Are Overkill
Before we dive into patterns, let's address the elephant in the room: you probably don't need an agent. I know, not what you expected to hear. But I've watched too many teams jump straight to building complex agent systems when a simple prompt with good retrieval would do the job better.
The first question isn't "how do we build an agent?" but rather "do we need an agent at all?" Could a well-crafted prompt handle this? Would adding basic tool access be enough? Is the complexity worth the tradeoff in latency and cost?
Remember: agents aren't free. They trade speed and simplicity for capability. Make sure that trade makes sense for your use case.
Simple Patterns That Actually Work
When you do need agents (and sometimes you genuinely do), I've found these patterns consistently deliver results:
1. The Chain of Prompts
This is the simplest pattern, and often the most effective. Break your task into clear steps, validate each step's output, and feed it into the next step. Think of it like a pipeline: process the initial input, validate the results, build on those results, and continue step by step.
I've seen this pattern work beautifully for content generation and refinement, multi-stage data processing, and complex decision workflows. The key is maintaining clear validation points between steps to catch issues early.
2. The Router
Sometimes the key to effectiveness isn't clever logic – it's knowing when to delegate. A router pattern is dead simple: it looks at the input and decides where it should go. This shines when different requests need different handling, when you want to optimize costs by routing simple queries to smaller models, or when you need specialized expertise for different tasks.
