Multi-Agent Systems: Orchestration, LangGraph, and Production Patterns

A single-agent architecture is a single LLM running a ReAct loop with access to a tool set. For tasks up to ~5–8 steps, this works well. Beyond that, three failure modes compound and become the dominant engineering concern.

Context window saturation: Each observation from a tool call is appended to the growing context. A 10-step agent accumulates ~20K tokens of context. While GPT-4o supports 128K tokens, attention quality degrades significantly in the middle of long contexts (the lost-in-the-middle problem). The LLM starts ignoring earlier observations and reasoning inconsistently. GPT-4o at 100K context length has measurably worse performance on tasks requiring the beginning-context information than at 8K.

Error accumulation and brittle chains: Each reasoning step has some error probability ε. For a 10-step chain, the probability of at least one mistake is 1 - (1-ε)^10. If ε = 0.1 (a generous assumption for complex reasoning), a 10-step chain has a 65% probability of containing at least one error, and errors early in the chain contaminate all subsequent steps.

No parallelization: A single agent is inherently sequential. If answering a question requires researching 5 independent sub-topics, the single agent must research them one after another. A worker pool of 5 agents can do this in parallel, reducing total latency by up to 5×.

Single point of failure: If the agent's context becomes corrupted by a bad tool response or a prompt injection attack, there's no external oversight to catch it. The orchestrator-worker pattern adds that oversight layer.