Three-Phase AI Agent Workflow Cuts Hallucinations by Limiting Context

A structured three-phase workflow — Research, Plan, Implement — has been proposed to reduce AI agent errors caused by bloated context windows during long sessions. The approach, adapted from a HumanLayer talk, involves clearing the context between each phase and saving outputs as markdown files so the agent only needs the final conclusion, not the full history. Parallel subagents handle heavy reading tasks in isolated contexts and return only summaries, keeping the main agent's context usage below 40%. Cheaper models can be assigned to subagents while a more capable model orchestrates the overall task, balancing cost and performance. The author emphasizes that detailed requirements and explicit edge cases are critical, as agents tend to fill information gaps with assumptions that often miss the mark.