Couple months into building our first agentic system, I hit a wall. Our LangChain implementation was becoming a tangled mess of chains and agents, each adding its own slice of latency. The system worked, sure, but it felt like we were building a Rube Goldberg machine when what we needed was a scalpel.
That's when I started experimenting with different approaches, and what I learned changed how I think about agentic engineering entirely.
The Initial Approach: Framework Hell
Like many developers, I started with LangChain. The promise was compelling: pre-built chains, easy orchestration, and a robust ecosystem. What wasn't obvious at first was the cost we'd pay in complexity and performance.
Every chain added 1.2-1.6 seconds of latency. Costs spiraled quickly - what should have been simple operations were burning through tokens at 3-10x the rate of direct API calls. We were paying for flexibility we didn't always need.
The Direct API Revelation
Frustrated, I stripped everything down to direct API calls. The difference was immediate: sub-second responses, predictable costs around $0.002 per 1K tokens, and code that was actually readable. For simple, high-speed operations, this became our go-to approach.
But then came the complex reasoning tasks. Direct API calls alone couldn't handle the nuanced decision-making we needed. We needed something in between.
Discovering Reflective Models
That's when I stumbled upon O1 and Qwen QwQ. These reflective frameworks represented a different philosophy altogether - instead of chaining multiple API calls, they use internal self-reflection cycles to handle complexity in a single request.
My first experiments with O1-mini were eye-opening. Yes, it was slower - two to four seconds for thoughtful reasoning - and costlier ($0.10-$1 per request depending on the model). But the quality of output for complex tasks was worth it. The model could actually think through problems in a way that felt more human than mechanical.
(Quick side note: O1-mini has become my secret weapon. The combination of cost, speed, and that impressive 65K token output is hard to beat. I would probably favor Qwen if enterprise compliance wasn't a concern, but that's a different story.)
The Hybrid Approach That Actually Works
After months of trial and error, I've landed on a hybrid approach that feels right:
- Direct APIs for anything requiring speed (user interactions, simple transformations)
- Traditional frameworks when we need complex orchestration
- Reflective models for the heavy cognitive lifting
The key was learning to be pragmatic. Not every problem needs the same solution, and the best architectures are often the ones that know when to use each tool.
Real-World Impact
This approach has transformed our system's performance. Response times dropped by 60% for simple operations, while our more complex reasoning tasks actually got better results, even if they took a few seconds longer.
The cost savings were significant too. By reserving the expensive operations for where they actually matter, we cut our token usage by roughly half.
Looking Forward
The field of agentic engineering is moving incredibly fast, and what works today might not be optimal tomorrow. But the core lesson I've learned is timeless: don't get locked into a single approach. The best solutions come from understanding the trade-offs and being willing to mix and match based on real-world requirements.
For those building similar systems, my advice is simple: start with the simplest possible solution, measure everything, and don't be afraid to rip things out when they're not serving your needs. Sometimes the best architecture is the one that's willing to be pragmatic rather than pure.
