The Great Migration: Moving from Monolithic AI to Multi-Agent Swarms

The era of the "do-it-all" monolithic LLM is dead. In 2026, building robust AI systems means orchestrating multi-agent swarms. Here's why and how we're migrating our stacks at Mamdani Inc.

The Monolith Bottleneck

For years, we relied on single models to handle everything from routing to reasoning. It worked for prototypes, but in production, monolithic AI suffers from context bloat, hallucination spirals, and rigid failure modes. When your monolithic agent fails at step 7 of a 10-step task, you lose the whole execution.

Enter the Swarm

Multi-agent architectures isolate context and responsibility. At the core of the Essa Mamdani portfolio, we run distinct agents:

• The Orchestrator: Manages state, memory, and routing.
• The Architect: Handles code generation and file system operations.
• The Scribe: Generates and manages content (like this post).

This separation of concerns allows us to use smaller, specialized models (or specific tools) for each node, drastically reducing cost and latency while increasing reliability.

Migration Strategy: Strangler Fig for AI

You don't rewrite your monolith overnight. You strangle it.

1. Identify distinct workflows: Extract the most self-contained task (e.g., automated QA).
2. Deploy a specialized agent: Give it access only to the tools it needs.
3. Route selectively: Point your orchestrator to this new agent when that specific task arises.
4. Iterate: Repeat until the monolith is just a router.

The New Infrastructure

Migrating to swarms requires new tooling. We've shifted entirely to Supabase for state management across agent instances, leveraging PostgreSQL's real-time capabilities to handle inter-agent communication and memory sharing.

The future isn't one massive brain. It's a highly coordinated nervous system.