We're hitting the limits of "one agent + tools." The next problem is coordination?
When engineers first build AI agents, they typically rely on a single LLM running a ReAct (Reasoning and Acting) loop with access to multiple tools. This "God Agent" architecture collapses in production. As you add more tools, the context window bloats, reasoning degrades, and the single agent inevitably suffers from tool-selection paralysis or infinite loops.
The Shift to Multi-Agent Orchestration
To scale agentic automation, Platform Engineers must transition from monolithic agents to Multi-Agent Orchestration. Instead of one massive model doing everything, you deploy a swarm of specialized, heavily constrained micro-agents coordinated by a deterministic router.
🤖 Agentic Architecture Patterns
The Remediation Strategy
Implement a framework like LangGraph or AutoGen to build a Supervisor Pattern. The Supervisor agent receives the user query and does zero synthesis. Its only job is routing. It passes the task to the "SQL Extraction Agent" or the "Formatting Agent," collecting their outputs and returning the final result. This enforces strict separation of concerns, drops token costs, and makes debugging individual agent failures trivial.
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