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The Case for Multi-Agent Systems
Research
Featured
5 min read

The Case for Multi-Agent Systems

Anthropic's multi-agent system outperformed single-agent Opus by 90%. The reason isn't better models. It's that intelligence degrades when you ask one agent to do everything, and improves when you let specialists work in isolation.

Tejas Bhakta
Tejas Bhakta
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Latest Articles

Flash Compact: 33,000 tok/sec Context Compaction
Engineering

Flash Compact: 33,000 tok/sec Context Compaction

Flash Compact drops 50-70% of an agent's context at 33,000+ tokens/second while keeping every surviving line verbatim. Two modes: objective compaction strips filler with no guidance, query-based compaction weights keep/drop decisions against what the agent needs next.

Tejas Bhakta
Tejas Bhakta
Learning to Test Code Changes with RL
Research

Learning to Test Code Changes with RL

How we built a reward policy that trains browser agents to verify UI changes through code coverage and component engagement.

Tejas Bhakta
Tejas Bhakta