Why Single Agents Hit a Wall
Analyze the failure modes of single-agent systems on complex tasks: context exhaustion, tool overload, and lack of specialization, and understand when multi-agent architecture is warranted.
The Promise of Single Agents
Early AI agents were built with an appealing simplicity: one LLM, a set of tools, and a loop that runs until the task is done. For simple tasks like answering a question or fetching a web page, this works beautifully. The problem appears when you scale up the complexity of the task you are trying to solve.
Context Window Exhaustion
Every LLM has a finite context window that limits how much information it can consider at once. In a long-running agent task, the growing history of thoughts, tool calls, and observations eventually fills this window completely. When that happens the agent either truncates critical early context or halts with an error.
For example, a research agent analyzing 50 papers will accumulate tens of thousands of tokens of observations long before finishing.
# Context exhaustion example
max_tokens = 128000 # GPT-4o context limit
conversation_history = []
total_tokens = 0
for step in agent_steps:
step_tokens = count_tokens(step)
if total_tokens + step_tokens > max_tokens:
# Agent cannot proceed - context is full
raise ContextExhaustedError('Agent context window full at step ' + str(len(conversation_history)))
conversation_history.append(step)
total_tokens += step_tokens