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arjun

T3 · host / generalist

Co-founder of Trajectory, building a platform for continual learning for AI agents. Previously worked at Apple on differential privacy. Co-founded with Ronak (ex-One Surf, Train Speed 1) and Michael (ex-DeepMind robotics).

2 calls·1 name·100% bull·last heard last month·Sequoia Capital
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$TRAJECTORYTrajectoryposition

Trajectory co-founder argues continual learning closes AI agent experience gap

Trajectory builds a platform for continual learning that captures real agent interactions, converts them into model specs, and uses RL algorithms like SDPO to improve models and harnesses over time, enabling AI systems that compound with use rather than staying static.

Sequoia Capital2026-08episode →

most discussed · click a bar to filter

  • $TRAJECTORY

recurring themes

  • AI Agents3
  • AI Infrastructure3
  • AI Applications1
  • Frontier AI Models1
  • Open Source AI1
2 total
$TRAJECTORY
Trajectory
HIGHarjun·Sequoia Capital·last month·Closing the Experience Gap: Why Smart AI Agents Still Feel New on the Job | Trajectory· position
Trajectory co-founder argues continual learning closes AI agent experience gap
Trajectory builds a platform for continual learning that captures real agent interactions, converts them into model specs, and uses RL algorithms like SDPO to improve models and harnesses over time, enabling AI systems that compound with use rather than staying static.
"We're Trajectory and we're building the platform for continual learning. Who are we? I think we're working on like a really really cool mission and that's the most fun part. The s…"
0:29
$TRAJECTORY
Trajectory
MEDarjun·Sequoia Capital·last month·Closing the Experience Gap: Why Smart AI Agents Still Feel New on the Job | Trajectory
Trajectory builds continual learning platform to close AI agent experience gap
Trajectory captures agent interactions, converts them into model specs, and uses RL (SDPO) to improve both models and harnesses, enabling companies to own their intelligence layer and compound with use.
"We're Trajectory and we're building the platform for continual learning... we want to imagine what learning agents look like where as they are used by people, they get better and…"
0:29
8
AI Agentstailwind
Continual learning closes the experience gap for AI agents
Models are improving on IQ but lack experience; capturing real interactions and learning from them via RL enables agents that compound with use, creating a structural tailwind for AI agent infrastructure.
8
AI Infrastructuretailwind
Full-stack continual learning platform spans traces, evals, harnesses, and models
True continual learning requires optimizing across the entire agent system — traceability, production-grade evals, flexible harnesses, and model post-training — not just model weights.
8
AI Applicationstailwind
Continual learning drives exponential gains on frontier tasks where models barely succeed
The highest ROI for continual learning is on tasks at the edge of model capabilities where users push boundaries; models learn from these interactions to master previously impossible tasks, expanding the product's frontier.
7
AI Infrastructuretailwind
Traceability must capture full agent trees and corrective user behavior
Current tracing misses sub-agents and tool calls; the real learning signal comes from corrective edits, undos, and retries, not simple thumbs up/down, requiring product design that elicits and captures this behavior.
7
AI Infrastructuretailwind
Evals should be drawn from real production traffic and be replayable
Evaluation environments should match production harness exactly, using actual user traffic and frontier requests, with the ability to replay any user task for continuous improvement.
7
AI Agentstailwind
Harnesses should provide primitives for agents to orchestrate, not enforce flows
As agents become more capable, harnesses should shift from preventing errors to exposing tools and private info as primitives, letting agents orchestrate freely, with agent interfaces mirroring user interfaces and informative tool responses.
7
Frontier AI Modelstailwind
Open-weight models and model routers unlock continual improvement
Owning model weights via open-source bases and routing tasks to specialized models enables companies to continually post-train on their own data rather than relying on static frontier APIs.
7
Open Source AItailwind
Open weight models and routers unlock continual learning ownership
Companies must get comfortable running open weight models to own their weights and continually improve; model routers will route tasks to the right capability, enabling customized intelligence.