The Underrated Power of Boring Technology
At Robynn AI, we’re building cutting-edge AI agents. We use LangGraph for orchestration, Claude’s SDK for reasoning, OpenClaw for tool integration, and a dozen other libraries that didn’t exist two years ago.
Our database? PostgreSQL. Our cache? Redis. Our message queue? RabbitMQ.
This isn’t a contradiction. It’s a strategy.
The Complexity Budget
Every engineering team has a limited complexity budget. You can only absorb so much novelty before your system becomes unmaintainable, your debugging becomes guesswork, and your on-call rotations become nightmares.
The question isn’t “should we use new technology?” It’s “where should we spend our complexity budget?”
At Robynn, we spend it on AI. We’re using LangGraph to orchestrate multi-step agent workflows. We’re using Claude’s SDK with structured outputs to ensure reliable tool calls. We’re experimenting with new prompting techniques, new memory architectures, new ways of handling agent failures.
That’s where our competitive advantage lives. That’s where we should be on the cutting edge.
But storing data? Caching responses? Queuing background jobs? These are solved problems. We don’t need innovation here—we need reliability.
The Temptation of the New (A Confession)
I learned this the hard way. Early in my career at Riverbed, I pushed for MongoDB when PostgreSQL would’ve been perfectly fine. The allure was irresistible: “web scale,” document flexibility, the thrill of being on the cutting edge.
What actually happened? Some time later, we needed transactions. We needed relational queries. We needed the things that boring databases had solved decades ago.
The migration cost more than the original implementation.
The irony? If I’d saved that complexity budget, we could have used it on features that actually mattered to users.
What “Boring” Actually Means
When I say “boring technology,” I don’t mean outdated. I mean battle-tested.
PostgreSQL has 35+ years of edge cases discovered and fixed. When you Google an error at 2 AM, there’s a Stack Overflow answer from 2014 that still works. The documentation is comprehensive. The failure modes are well-understood.
Boring tech hasn’t failed—it’s graduated. It survived the hype cycle and emerged as infrastructure you can trust.
This is especially important when your application layer is doing something novel. When your AI agent does something unexpected (and they will), you need to debug the agent—not wonder if the issue is in your experimental database.
Boring infrastructure gives you stable ground to stand on while you experiment above it.
The Hidden Costs of Shiny Infrastructure
New infrastructure technology comes with costs that don’t show up in the README:
Documentation gaps. The docs assume you already understand concepts they haven’t explained. You’ll spend hours piecing together blog posts, GitHub issues, and Discord messages.
Hiring difficulty. Finding engineers who know CockroachDB is harder than finding engineers who know PostgreSQL. The ones who do know it? They’re expensive.
Operational unknowns. What happens at 3 AM when it breaks? With PostgreSQL, I can find a DBA who’s seen my exact error a hundred times. With the new hotness, I’m filing GitHub issues and hoping.
Migration tax. Technologies get abandoned. Companies pivot. When the shiny thing gets deprecated, you’re stuck with a migration you didn’t budget for.
When you’re already dealing with the operational unknowns of AI agents—which are substantial—you don’t want to add infrastructure unknowns on top.
Where We Do Use Cutting-Edge Tech
Let me be clear: we’re not Luddites at Robynn. Here’s our actual stack:
Cutting-edge (where it matters):
- LangGraph for agent orchestration—because multi-step AI workflows are genuinely new territory
- Claude SDK with structured outputs—because reliable tool calling requires the latest capabilities
- OpenClaw for tool integration—because the AI tool ecosystem is evolving rapidly
- Vector databases for semantic search—because embedding-based retrieval is a new requirement
- Custom prompt engineering patterns—because we’re discovering best practices in real-time
Boring (where it doesn’t):
- PostgreSQL for relational data—because ACID transactions are a solved problem
- Redis for caching and simple queues—because it’s fast and predictable
- S3 for blob storage—because it just works
- Linux, Nginx, Python for compute—because debugging is easy
The key insight: we’re cutting-edge in our product (AI agents) and boring in our infrastructure (data storage, caching, deployment).
My Heuristic: “Where Does Innovation Create Value?”
Before adopting any new technology, I ask two questions:
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Does this directly improve what customers pay us for? If we use a better orchestration framework, our agents work better. That’s value. If we use a fancier database, our data is stored… the same. That’s not value.
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Would I bet my 3 AM sleep on this? New AI tools? The team expects some rough edges—it’s the nature of the space. New infrastructure? Nobody wants to debug a mysterious database failure while also debugging agent behavior.
For AI/ML tooling, the answer is often “yes, the innovation is worth it.” For infrastructure, the answer is almost always “no, use the boring thing.”
The Boring Stack That Enables Innovation
Here’s the pattern I recommend:
Infrastructure layer (boring):
- PostgreSQL or MySQL for relational data
- Redis for caching
- S3 for files
- Standard cloud compute (EC2, Cloud Run, etc.)
Application layer (as innovative as you need):
- Whatever frameworks make sense for your problem
- Cutting-edge AI/ML libraries if that’s your domain
- New tools that directly improve your product
This layering matters. When something breaks, you want to know immediately whether it’s an infrastructure issue (unlikely with boring tech) or an application issue (likely with cutting-edge stuff). Clear separation makes debugging tractable.
The Meta-Lesson
The engineers I respect most aren’t the ones chasing every new framework. They’re the ones who understand where to innovate.
They use boring databases and exciting product architectures. They use reliable infrastructure and experimental features. They save their complexity budget for problems that actually differentiate their product.
At Robynn, we’re pushing the boundaries of what AI agents can do. We’re experimenting with new orchestration patterns, new memory systems, new ways of handling uncertainty.
We can do that because we’re not also experimenting with our database. Our boring infrastructure gives us the stability to be bold where it counts.
Innovation should happen in your product, not your infrastructure. Save your complexity budget for problems your customers actually pay you to solve.
The best engineers aren’t the ones using the newest everything. They’re the ones who know which battles to pick.
If you’re building AI agents and thinking through these tradeoffs, I’d love to chat. We’re learning a lot at Robynn and always happy to share. Reach out at architgupta941@gmail.com or find me on X.
