Hey HN, we've been running multi-agent orchestration pipelines at Frequency for a few weeks (wrote about the architecture previously on our blog). The recurring pattern we see is that most existing software resists agent operation in ways you only discover at scale. The friction is rarely in the API layer. It's embedded in the product's fundamental assumptions about the user being human: the data model, the interaction patterns, the state management.
What prompted this essay was our own trouble in utilising agents with exisitng tools resulting in us rebuilding products from the ground up. That made us think of the question of what happens when my customer is an agent shopping for the best tool to accomplish a human's task (which has been a question in a lot of peoples mind around the openclaw hype, whether openclaw is actually useful is another question). We think that reframing changes which products survive the transition.
Interested to hear from anyone else running agents against existing tools at scale. Are you seeing the same friction points, or has the adapter approach been working well enough for your use cases?
We've seen this exact pattern. Most devtools assume a human will eventually log in and contextualize the data. When the 'user' is an agent, you need the surface to be machine-readable by default, not as an afterthought. The adapter approach mostly doesn't work ie you end up with a translation layer that loses exactly the signal you needed.
Hey HN, we've been running multi-agent orchestration pipelines at Frequency for a few weeks (wrote about the architecture previously on our blog). The recurring pattern we see is that most existing software resists agent operation in ways you only discover at scale. The friction is rarely in the API layer. It's embedded in the product's fundamental assumptions about the user being human: the data model, the interaction patterns, the state management.
What prompted this essay was our own trouble in utilising agents with exisitng tools resulting in us rebuilding products from the ground up. That made us think of the question of what happens when my customer is an agent shopping for the best tool to accomplish a human's task (which has been a question in a lot of peoples mind around the openclaw hype, whether openclaw is actually useful is another question). We think that reframing changes which products survive the transition.
Interested to hear from anyone else running agents against existing tools at scale. Are you seeing the same friction points, or has the adapter approach been working well enough for your use cases?
We've seen this exact pattern. Most devtools assume a human will eventually log in and contextualize the data. When the 'user' is an agent, you need the surface to be machine-readable by default, not as an afterthought. The adapter approach mostly doesn't work ie you end up with a translation layer that loses exactly the signal you needed.
its like devtools are now agenttools
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