For AI Agent Fleets, the Model Is Rarely the Bottleneck | Theory Ventures' Andy Triedman

Andy Triedman is a partner at Theory Ventures, and he is building his own software factory, while assessing how startups are leveraging theirs. His read after running agent fleets: the models are frankly good enough for most of the work. The vast majority of things you'd ask an agent to do are not intelligence limited. They are context limited. We have infrastructure work to do - but we can be more ambitious!

So the hard part moves upstream. Deciding what the agents should build, what they need to know, and when they should stop and ask a person is now most of the job.

We cover:

• Moving from solving one problem at a time to building systems that solve whole classes of problems, like generating and testing integrations
• Which tasks still need human approval, and which can be fully handed off
• Why a short feature spec leaves too much unsaid, and how better questions up front cut review interruptions
• Keeping shared context and company decisions current, because an agent takes everything it reads as true
• Inference ROI, and when well-scoped tasks can go to cheaper models
• Sandboxes and simulated customer environments for agents
• First steps for teams: shared skills, cloud agents and approval gates
• Protecting customer data, and organizing agent memory from session traces to company priorities

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• Theory Ventures

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Thanks to G2i for sponsoring this episode - for over a decade, they vetted and placed engineers at other companies, from startups to FAANG. Two years ago, they turned that same judgment inward, building their own bench to review RL environments, evals, and training data that models are trained on. Get access