This connects directly to the split I'm seeing between task agents and reasoning agents.
Task agents don't need sophisticated context engineering — they get a well-defined input, run a workflow, produce output. The context is implicit in the schema.
Reasoning agents are where context engineering becomes make-or-break. They need: (1) what happened before this turn, (2) what the user actually wants (not just said), (3) what constraints exist that weren't stated, (4) what the agent tried already.
The reason most subagent architectures fail isn't the orchestration — it's that each subagent is operating with incomplete context of what the others are doing. You can chain LLM calls together but if agent B doesn't know why agent A made decision C, you get compounding context drift.
The teams cracking this are treating context as a first-class engineering problem, not an afterthought to prompt design.
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This connects directly to the split I'm seeing between task agents and reasoning agents.
Task agents don't need sophisticated context engineering — they get a well-defined input, run a workflow, produce output. The context is implicit in the schema.
Reasoning agents are where context engineering becomes make-or-break. They need: (1) what happened before this turn, (2) what the user actually wants (not just said), (3) what constraints exist that weren't stated, (4) what the agent tried already.
The reason most subagent architectures fail isn't the orchestration — it's that each subagent is operating with incomplete context of what the others are doing. You can chain LLM calls together but if agent B doesn't know why agent A made decision C, you get compounding context drift.
The teams cracking this are treating context as a first-class engineering problem, not an afterthought to prompt design.