Microservices promised us the moon: scale independently, deploy separately, team autonomy, polyglot freedom. We got complexity instead. Distributed systems are hard. Really hard.

We spent 15 years building sophisticated orchestration layers, service meshes, and debugging tools just to manage the chaos we created trying to avoid monoliths.

Now? The smartest architectures aren’t microservices. They’re agentic monoliths.

The Microservices Hangover

Let me be honest: microservices made sense in 2015. Your biggest problem was scaling. Your second biggest was team velocity—carving up a monolith so teams could own pieces independently.

But the cost was astronomical:

• Distributed system complexity. Network calls, timeouts, partial failures, eventual consistency. You just moved your business logic into a state machine called “debugging.”
• Operational overhead. Docker, Kubernetes, service discovery, circuit breakers, observability dashboards everywhere. You need an entire infrastructure team just to keep the lights on.
• Data synchronization. One service owns users, another owns orders, another owns inventory. They need to sync. Good luck with that at 3am.
• Development friction. Local development? You’re spinning up 12 services. Changes to one service might affect three others. Integration testing takes 20 minutes.
• The real kicker: You still can’t reason about the whole system because it’s distributed across 200 services with inconsistent APIs, versions, and patterns.

Meanwhile, people kept starting with monoliths because they were pragmatic. One database. One transaction boundary. One deployment unit. Simple to reason about.

Enter the Agent

Now we have something new: LLMs that can understand entire codebases. Read 100 files in context. Reason about database schemas, API contracts, and system state together. Make changes across multiple files while maintaining consistency.

This changes the calculus entirely.

An agentic monolith is:

• One codebase. Still monolithic at the database/deployment layer.
• One agent. An AI system with full read/write access to the codebase, with enough context window to understand the whole picture.
• Distributed execution. The agent can reason about which parts should run where (and orchestrate them), but it’s coordinating a unified system, not 12 independent services.

It’s like having a principal engineer who:

• Knows every line of code
• Can refactor across the entire codebase atomically
• Understands the full system architecture
• Catches cross-cutting concerns before they become problems
• Can parallelize work without breaking invariants

Why This Wins

Reasoning Scope. Microservices force you to reason in service-sized chunks. An agent reasons about the entire system. It sees that changing a user schema in service A affects three places in service B. It updates all of them in one atomic operation.

Consistency. Transactions work again. You can update related data atomically instead of relying on eventual consistency and complicated saga patterns.

Speed of Development. Most startups don’t actually need to scale to Netflix’s problem space. They need to move fast. A monolith is faster. Vastly. An agentic monolith is a monolith that improves itself.

Operational Simplicity. One deployment pipeline. One database. One set of monitoring dashboards. Your infrastructure team can actually sleep.

Coherence. The agent doesn’t just write code—it maintains coherence. Same error handling patterns everywhere. Consistent API design. Shared utilities. No drift between services because there’s one system that understands the whole thing.

The Catch (There’s Always a Catch)

Agentic monoliths aren’t a silver bullet. They have real limitations:

Scalability. Eventually, you’ll need to shard or scale horizontally. An agentic monolith handles this differently than microservices—the agent designs the architecture, then manages it—but it’s still non-trivial.

Team Boundaries. Microservices were about letting teams own pieces independently. If you hire 50 engineers, you can’t have them all working on one codebase coherently. But most companies? Most teams? 5-15 engineers working on one system is fine. And the agent scales this better than humans would.

Legacy Integration. You still need to talk to third-party services, legacy systems, payment processors. Agentic monoliths are still systems—they have APIs out, databases in, etc.

Agent Limitation. Today’s agents can reason about 100-200k tokens of context. That’s a lot, but it’s not infinite. A codebase with 10 million lines of code? The agent can still navigate it (file structure, search, semantic indexing), but it can’t hold it all in context at once. The architecture has to account for this.

What Agentic Monoliths Look Like in Practice

// One codebase. The agent understands:
// - User service (lib/users/)
// - Order service (lib/orders/)
// - Inventory service (lib/inventory/)
// - Shared types and utilities
// - Database schema
// All together. In one transaction boundary.

const handleNewOrder = async (userId: string, items: OrderItem[]) => {
  return db.transaction(async (tx) => {
    // Agent reasons about all of this together:
    const user = await tx.users.findById(userId); // exists? has payment?
    const reservation = await tx.inventory.reserve(items); // available?
    const order = await tx.orders.create({
      userId,
      items,
      reservationId: reservation.id,
    });
    // No eventual consistency. No saga. No service-to-service API calls.
    // One agent, one logical operation, one transaction.
    return order;
  });
};

The agent doesn’t just write this code—it understands that changing the order schema affects the inventory reservation, which affects billing, which affects the user’s payment history. It updates all of them atomically.

The Real Insight

The future of software isn’t more services. It’s smarter systems. Systems that can reason about themselves, refactor themselves, and maintain coherence as they grow.

Microservices were optimized for a world where humans had to partition systems. Now? We have agents that can see the whole picture. That changes everything.

The optimal architecture isn’t determined by how you want to scale your infrastructure anymore. It’s determined by: How do I enable an agent to reason effectively about my entire system?

For most companies, that answer is: monolith with an agentic orchestration layer.

Turns out, 2005 had it more right than 2020.

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Notes

• This doesn’t mean monoliths are returning unchanged. They’ll have observability, feature flags, and canary deployments built in by default—things that were afterthoughts in 2005.
• Agentic systems will still need to interface with distributed infrastructure (cloud providers, databases, queues). But the coherence is where the agent wins.
• The real killer app for agentic monoliths? Legacy system modernization. An agent that understands your entire 20-year-old codebase and can refactor it safely. That’s worth more than 100 microservices.