Continual Learning: How AI Agents Get Better With Every Use | Arjun Karanam, Trajectory
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Today's AI models keep getting smarter, but every conversation still feels like their first day on the job. Trajectory co-founder Arjun Karanam calls this the experience gap: models are improving on IQ, but not on experience. Continual learning aims to close it, turning the trillions of tokens of agent interactions that get thrown away into signal that makes agents better with every use. At Sequoia Capital’s Own Your Intelligence event, Arjun lays out four goals for companies that want to get there: full traceability (including sub-agents and corrective behavior like edits and retries), evals drawn from real production traffic, harnesses that let agents orchestrate rather than constrain them, and getting comfortable running on open weights. He also covers what should be trained into the model versus left to the harness, and how to learn from interactions without training on customer data. 00:00 Introduction 00:12 Building the platform for continual learning 01:33 The experience gap: models have IQ but no tenure 02:52 Traceability → model spec → better models and harnesses 05:27 Four wishes for the agent ecosystem 06:34 Wish 1: Trace the whole tree — and capture the corrections 08:03 Wish 2: Evals from real traffic, graded in the real harness 09:26 Wish 3: Let the agents cook, and make tool responses informative 10:34 Wish 4: Get comfortable on open weights, experiment with routers 11:51 Why owning your intelligence shouldn't be consulted away 13:15 Demo: import a benchmark, train a model, deploy it 14:28 Q&A: What's the trainable object — weights, harness, or context? 16:08 Q&A: Continual learning without training on customer data 17:13 Q&A: Episodic memory and the hierarchy of feedback 19:37 Q&A: Where continual learning matters most
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