Agentsystems

Two frontier teams published production data that directly contradicts each other. Cognition says multi-agent architectures cause compounding information loss and single-threaded execution is the fix. Anthropic says multi-agent delegation beat a single agent by 90.2% on their research eval. Both are right—for the model version they tested on. Architectural best practices in agent systems have a six-month half-life.

One team running a six-agent debate system switched to two agents with a strict state machine. Latency dropped from 18 seconds to 3. Cost per query dropped from $8-12 to $0.40. Accuracy changed by less than 1%. This wasn't a fluke. Information theory explains exactly why multi-agent systems can never outperform a single agent with full context.

If your RAG pipeline runs the same retrieval strategy for every query, you're in one corner of a five-dimensional design space—and it's the worst-performing corner. Static pipelines leave up to 15% accuracy on the table and spend 3x the tokens they need to. The move to agentic retrieval is incremental, and each step compounds with every model generation.

An 8B-parameter model matches GPT-4o across five knowledge-intensive benchmarks and beats it on two. It does this by replacing the entire retrieval orchestration layer—confidence classifiers, query routers, rerankers, fusion logic—with four special tokens. No external components. When it fails, you read a transcript. That's the whole debugging story.

Somewhere in your codebase there's a pipeline nobody touches. It was built because the model couldn't do something. But the model can now, and yet the pipeline still runs. The problem isn't awareness. It's that adding a component has a clear champion and deletion has none. Here's what it actually takes to make "build to delete" a practice instead of a principle.