Lessons from 25 Trillion Tokens — Scaling AI-Assisted Development at Kilo

2026-03-24 · Source: MLOps.community · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

Kilo, an all-in-one agentic engineering platform, has processed over 25 trillion tokens since its launch in May, demonstrating a significant transformation in developer team capabilities. The company, founded in March, has observed its internal team shift from shipping one feature every two to three weeks to one to two features weekly, without increasing team size or work hours. This acceleration is attributed to developers evolving into orchestrators of AI agents, focusing on end-to-end feature ownership and minimizing traditional collaboration. Kilo emphasizes a "proof is in production" philosophy, shipping fast with only 15 engineers. The company's experience with 1.5 million developers highlights an "AI adoption ladder," where trust is gradually built from autocomplete to chat, single agents, and finally, full orchestration, with latency and irrelevant suggestions being critical trust breakers.

Key takeaway

For AI Engineers aiming to accelerate development cycles, embrace the shift towards becoming an orchestrator of AI agents rather than a pure coder. Focus on providing comprehensive context to your AI tools and strategically combine different models for specific tasks to optimize both cost and performance. Continuously monitor user trust signals, such as acceptance rates for AI suggestions, to quickly identify and resolve issues that could erode confidence and slow adoption within your team.

Key insights

Developer roles are shifting from coders to AI orchestrators, dramatically increasing feature velocity and requiring deep trust in AI tools.

Principles

• Minimize collaboration to boost individual engineer velocity.
• Trust in AI tools is built incrementally and easily broken by poor performance.
• Context is paramount for effective AI agent performance.

Method

Adopt an "AI adoption ladder" approach, starting with simple AI tools like autocomplete, progressing to chat, single agents, and finally, full orchestration, while continuously monitoring and optimizing for trust.

In practice

• Implement end-to-end feature ownership for engineers.
• Use cost-effective models (e.g., Kimmy, Miniax) for coding/debugging.
• Instrument AI features to measure user trust and acceptance rates.

Topics

• Agentic Engineering
• AI Adoption
• Developer Productivity
• Large Language Models
• Software Development Workflow

Best for: Software Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by MLOps.community.