In-product human-to-human chats. 

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Customer conversations. 

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Agent-to-Agent conversations. 

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Employee-to-Agent conversations. 

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More conversations recorded. 

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Synthetic text datasets.

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The way Splunk owned system logs. 

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The way CrowdStrike owned security logs.

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There will be an application that owns conversation logs.

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We call that a Conversation Data Platform.

When you use Claude Code, you see the same model (ex. Opus 4.6) is used across Claude Code, Claude Cowork, and Claude Chat. Then why does it perform differently? The tools and environment.

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Think of the model as the brain and the tools as the arms, hands, legs, and feet.

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Claude Code has many coding-specific tools. 

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What you need for your task will require specific tools, environments, and optimizations too.

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Designing the best tools and environments is going to be the name of the game.

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For example, if you want to build an analytics system, you connect Claude to Snowflake. In that case, Snowflake is the tool. Snowflake is the hard part of the problem and 99% of companies don’t have the engineers to build products as technically difficult as Snowflake.

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This has become more obvious since the code behind Claude was leaked the other week and people studied it - but it’s not obvious to the regular person blown away by Claude Code and “Vibe Coding”. When you experience a crazy, mind-blowing output, it's hard to separate the 'AI' from the tools and systems doing the mission-critical heavy lifting.

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The world of Tool Design is going to go through a lot of trial, iteration, and realization — we have our differentiated POV but the playbook is being written.

In my prior post, I talked about the Model versus Tools. The next interesting point around tools is who is going to be the primary user of them.

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Pre-2026, the primary users of these tools were humans. Post-2026, it will be AI Agents.

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Let’s say a human currently goes through 10 steps to achieve a goal. AI will eat away at each of those steps so that a human only has to do 8 steps, then 5 steps, and so forth — with the holy grail being 100% automation. 

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This is far more complicated than it sounds, but it gives you a sense of where products are pushing.

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A key part is connecting a tool to as many different AI agents as possible.

Tactically, Rippit has tools to analyze conversation data. We use our own “brain” to leverage the tools, but we also want to make those tools available to other AI agents in other products. For example, Claude Code or Cursor should be able to use our tools to get customer insights for building features, and Figma, Replit, or Lovable should leverage customer insights to better design user experiences or marketing assets.