Million Step Agent Tasks

Can an AI agent complete over 1 million steps without a single error? The MAKER system proves it's possible. This clip from AIFS Weekly News (27 Feb 2026) dives into the "context rot" problem — where LLMs inevitably go off the rails during extremely long tasks — and how MAKER solves it through extreme decomposition into micro-agent sub-tasks and a multi-agent voting scheme for error correction at every step. It's a significant milestone for long-horizon agentic workflows and a blueprint for building reliable AI systems at scale.

The key insight: modularity is everything. By breaking a massive task into focused, manageable pieces and applying ensemble voting mechanisms at each stage, MAKER achieves what monolithic prompting simply can't — sustained accuracy over a million sequential LLM calls. This has huge implications for autonomous agent architectures and complex workflow automation.

Source: https://arxiv.org/abs/2511.09030

In this episode, we also cover: OpenAI's GPT-5.3-Codex with mid-execution 'steering' features, Ant Group open-sourcing trillion-parameter models (Ring-2.5-1T hitting IMO gold-medal territory), Anthropic's allegations of industrial-scale distillation attacks by DeepSeek, and IBM stock dropping after Claude Code gains COBOL modernization capabilities.

Full episode: https://youtu.be/ycTg7GnNuCg
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Видео Million Step Agent Tasks канала The AI First Show