"Every time you extract a collaboration between objects to a collaboration between systems, you're accepting a world of hurt with a myriad of liabilities and failure states"
— DHH (Creator of Ruby on Rails)
For a decade, we were told that Microservices were the only way to scale. We broke our systems into tiny, independent pieces so our human teams could work without stepping on each other's toes. We optimized for Human Scaling.
In the era of AI Agents, that architecture has become a liability.
In my previous post on State Machines, I argued that reliability comes from Control Flow. But even the best State Machine will fail if its "world view" is fragmented across 50 different microservices. AI agents don't care about your team structures; they care about Context. In 2026, the best move is the Modular Monolith. Here is why.
1. The "Network Hop" Reasoning Tax 🏎️💸
In a traditional system, a 50ms network delay is annoying. In an AI Agent loop, it’s a catastrophe.
If an agent needs to perform a complex task that requires data from four different services (e.g., Billing, Inventory, Shipping, and User Profile), it’s not just four API calls. It’s four potential points of failure and four different "state snapshots" that might be out of sync.
When your data lives in a Modular Monolith (a single database, like the Postgres-first approach), the agent has "Universal Memory". It can query the entire state of the system in microseconds.
2. Distributed Debugging is an Agent Killer 🕵️♂️
Try debugging an autonomous agent that is stuck in a logic loop across three different microservices.
You end up with a distributed trace that looks like a bowl of spaghetti. You spend your afternoon jumping between logs, trying to figure out why the "Payment Agent" thought the "Inventory Agent" was lying.
In a monolith, your "Flight Recorder" is unified. You can see the agent's exact thought process and every piece of data it touched in a single stream. As I mentioned when discussing State Machines, if you can't trace an agent's logic in one place, you can't govern it.
3. Human Scaling vs. Agent Scaling 🤖👥
We built microservices because humans are the bottleneck. A developer can’t hold a million-line codebase in their head, so we broke it into tiny pieces.
But in 2026, our AI tools can handle that codebase. They can navigate a monolith with ease. The bottleneck is no longer "human comprehension"; it’s system coherence. If your AI can understand the entire system at once, why are you paying the "Distributed Tax" of managing 50 Kubernetes pods and 50 CI/CD pipelines? You are solving a problem (human cognitive load) that your tools have already fixed.
4. The "Modular Monolith" is the New Flex 💎
This isn't "Spaghetti Code". A Modular Monolith is a system with strict internal boundaries but zero network boundaries.
Internal Modules: Use the compiler/language to enforce boundaries, not HTTP.
Shared Memory: Let your agents access data at the speed of RAM.
Unified State: Keep your ACID compliance. Stop worrying about "eventual consistency" when agents need "immediate truth."
1. The "Incident" Check: What is the most painful bug you’ve had to debug where an AI agent got "lost" in your microservice architecture?
2. The Migration Reality: Is anyone else quietly moving their AI services back into a single repo? What was the "final straw"?
3. The Human Factor: Are we ready to admit that microservices were a "social fix" for team management, or do they still have a technical advantage for AI?
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